From fbc417662c26da6c6d35ed2a7314559efa97feba Mon Sep 17 00:00:00 2001 From: Chen Date: Wed, 28 Oct 2026 06:59:49 -0400 Subject: [PATCH] Draft results: performance summary, draft report (Figs 1-3, Tables 2-5), D52 open questions Co-Authored-By: Claude Opus 5.5 --- docs/decisions.md | 6 + reports/draft-report.Rmd | 287 + reports/draft-report.html | 19479 +++++++++++++++++++++++++++++++++++ simulations/04_summarise.R | 90 + 4 files changed, 19862 insertions(+) create mode 100644 reports/draft-report.Rmd create mode 100644 reports/draft-report.html create mode 100644 simulations/04_summarise.R diff --git a/docs/decisions.md b/docs/decisions.md index ae71ab9..0c64a5b 100644 --- a/docs/decisions.md +++ b/docs/decisions.md @@ -103,3 +103,9 @@ Each decision is listed with the date and the reason for it. Settled decisions a | 49 | **V2 is the V1 fit with a physician-cluster-robust SE.** Under V4, physicians without an estimable fixed effect in a phase get probability 0, and the V4 population is restricted to physicians with variation in early PVI. | Same point estimate by construction (D15). | | 50 | **Validation (W0, harm, 40 replicates of 50,000):** CCW A−B1 is +5.12 vs a truth of +5.11 on the disease composite (z = 0.0); sequential ITT is +5.83 vs +5.71 (z = 0.6); MALE and NCO-1 have |z| ≤ 0.7. The model SE is 0–20% below the empirical SE for CCW, so the bootstrap check in D28 still needs doing. | Plan §15 validation (a). | | 51 | **Speed and scale.** The PVI model is fitted once per replicate and variant, and shared by CCW and the sequential trials. CCW clones and weights are built once for all outcomes. The deferred-period PVI model uses every PVI plus a fixed 10% sample of the other person-months, with offset log(10). On W0 validation the estimates are identical to 4 decimals. The main designs take about 39 s per replicate instead of about 130 s. **Scale:** n = 50,000 stays. Draft is 100 replicates; final is 200 replicates (replacing 500 in D29), and the final run extends the draft's replicates 1–100. Total about 12 hours on 11 cores. | User asked for a faster, smaller run. The sample size is not reduced, because CCW's strategy A already depends on about 250 patients treated in month 5 at n = 50,000. With 200 replicates, MCSE of bias ≈ empirical SE / 14 (CCW about 0.2 pp) and MCSE of coverage ≈ 1.5%. | + +## Draft run (2026-10-28) + +| # | Decision | Why | +|---|---|---| +| 52 | **Draft run done:** 8 worlds × 3 scenarios × 100 replicates of 50,000 patients; 9.4 h on 11 cores; 0 errors. 16 of 288,000 estimates are NA (sequential trials, V4, 4 replicates) and are dropped. Summary: `simulations/04_summarise.R` writes `results/summary/`. Report: `reports/draft-report.Rmd`. Two open questions: (Q1) the landmark propensity model adjusts for month-5 severity, a post-PVI mediator, which drives the landmark question gap (W1 own truth about 20 pp vs a reference of 7.2 pp); with baseline covariates only, the 20-replicate oracle mean is 8.1. (Q2) NCO-1 has no severity term in the DGP, contrary to D32, so it cannot detect W5/WR bias. The bootstrap check (D28) waits on Q1/Q2. | Surfaced by the draft results. | diff --git a/reports/draft-report.Rmd b/reports/draft-report.Rmd new file mode 100644 index 0000000..d29b446 --- /dev/null +++ b/reports/draft-report.Rmd @@ -0,0 +1,287 @@ +--- +title: "Early vs delayed PVI for claudication: known-truth simulation, draft results" +subtitle: "Draft scale: n = 50,000 patients, 100 replicates per world x scenario" +date: "`r format(Sys.Date())`" +output: + html_document: + toc: true + toc_float: true + self_contained: true +--- + +```{r setup, include = FALSE} +knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE, fig.align = "center", dpi = 120) +suppressPackageStartupMessages({ library(data.table); library(ggplot2); library(patchwork) }) +root <- normalizePath("..") +S <- file.path(root, "results", "summary") +perf <- fread(file.path(S, "perf.csv")); nc <- fread(file.path(S, "nc_reps.csv")) +ncs <- fread(file.path(S, "nc_summary.csv")); reps <- fread(file.path(S, "reps.csv")) + +WORLDS <- c("W0", "W1", "W1_rho0", "W2", "W3", "W4", "W5", "WR") +WLAB <- c(W0 = "W0 Clean", W1 = "W1 Skill", W1_rho0 = "W1 Skill (rho 0)", W2 = "W2 Style", + W3 = "W3 Style -> outcome", W4 = "W4 Deferral", W5 = "W5 Unmeasured severity", WR = "WR Realistic") +VAR <- c(V2_cluster = "Ignore physician (cluster SE)", V3_random = "Random intercept", + V4_within = "Within-physician (FE)", none = "Trial (no adjustment)") +VCOL <- c("Ignore physician (cluster SE)" = "#2a78d6", "Random intercept" = "#eb6834", + "Within-physician (FE)" = "#1baf7a", "Trial (no adjustment)" = "#52514e") +METH <- data.table( + k = c("ccw|A_B1", "ccw|A_B2", "seq|now_not", "landmark|early_none", "naive|early_none", + "rct_realistic|itt", "rct_ideal|itt", "rct_realistic|as_treated", "rct_ideal|as_treated"), + method = c("CCW, A vs B1 (reference)", "CCW, A vs B2", "Sequential trials, ITT", "Landmark at 6 months", + "Naive 'ever early PVI'", "Realistic RCT (n = 240), ITT", "Ideal pragmatic RCT, ITT", + "Realistic RCT, as-treated", "Ideal RCT, as-treated")) +setnames(METH, "k", "key") +pp <- function(x, k = 1) formatC(100 * x, format = "f", digits = k) +theme_set(theme_minimal(base_size = 11) + theme(panel.grid.minor = element_blank(), + panel.grid.major.y = element_blank(), strip.text = element_text(face = "bold", hjust = 0), + legend.position = "bottom")) +perf <- perf[analysis %in% c("main", "rct")] # NCE analyses are used only in Table 4 +perf[, key := paste(design, contrast, sep = "|")] +perf <- merge(perf, METH, by = "key") +perf[, method := factor(method, levels = rev(METH$method))] +perf[, wlab := factor(WLAB[world], levels = WLAB[WORLDS])] +perf[, vlab := factor(VAR[variant], levels = VAR)] +``` + +# Read this first + +* **Run.** 8 worlds × 3 truth scenarios × 100 replicates of 50,000 patients (about 1,500 physicians per + replicate). Each replicate runs naive, landmark, CCW and sequential trials under 4 physician + variants, 3 negative controls, an oracle run, and 2 simulated RCTs. 2,400 replicate files, 0 + errors. 16 of 288,000 estimates are missing (sequential trials with physician fixed effects, 4 + replicates), and these are dropped. +* **Outcome shown.** The headline outcome is the **disease composite (CLTI or major amputation)**, + whose true effect under the null is exactly 0 (D34). MALE is shown where it adds something. +* **Two errors, kept separate (D27).** *Estimation bias* = mean estimate − the method's own truth. + *Question gap* = the method's own truth − the reference truth (CCW A vs B1). *Total error* is the sum. +* **Variants shown.** "Ignore physician" is V1's point estimate with the cluster-robust SE (V2). + The V1 patient-level SE is in the supplement tables. +* **Two open questions** came out of this run (Section 6). They affect the landmark results and the + negative-control results, so treat those parts as provisional. + +# 1. Aim 1: what each method answers (clean world W0) + +```{r tab2} +t2 <- perf[world == "W0" & outcome == "disease" & variant %in% c("V2_cluster", "none") & + !grepl("as_treated", key)] +t2 <- t2[, .(Scenario = scenario, Method = as.character(method), `Reference truth` = pp(ref), + `Own truth` = pp(own), `Question gap` = pp(gap), `Mean estimate` = pp(mean_est), + `Estimation bias (MCSE)` = sprintf("%s (%s)", pp(bias, 2), pp(mcse_bias, 2)), + `Empirical SE` = pp(emp_se), `Coverage of own truth` = sprintf("%.0f%%", 100 * cover_own))] +t2[, Scenario := factor(Scenario, levels = c("null", "harm", "benefit_harm"))] +t2[, Method := factor(Method, levels = METH$method)] +knitr::kable(t2[order(Scenario, Method)], align = "llrrrrrrr", + caption = "Table 2. Disease composite, 5-year risk difference in percentage points, world W0.") +``` + +**What it shows.** + +* Every TTE method is close to unbiased for **its own** truth in W0 (all |bias| < 0.35 pp). H1a holds. +* The methods answer **different questions**. Under harm (reference +5.1 pp): sequential trials target + +5.7, landmark +7.9 and naive +7.2. So the question gap alone is 0.6–2.8 pp. Under the null, naive + has a gap of +1.3 pp from immortal time even with every confounder measured. H1b holds, though the + gap for the sequential trials is small. +* **CCW is right but imprecise.** Its empirical SE is about 2.8 pp at n = 50,000, against about 1.5 pp for + sequential trials. Under the null its 95% CI excludes 0 in 15% of replicates (Section 3). +* The **realistic RCT (n = 240)** is unbiased for its own ITT, but its SE is about 4.2 pp. It detects + the 5-point harm in only 25% of replicates. + +```{r fig1, fig.width = 11, fig.height = 9} +ladder <- function(sc, oc = "disease") { + x <- perf[scenario == sc & outcome == oc & variant != "V1_ignore" & !grepl("as_treated", key)] + r <- reps[scenario == sc & outcome == oc & analysis %in% c("main", "rct") & variant != "V1_ignore" & + variant != "oracle" & !grepl("as_treated", contrast)] + r[, key := paste(design, contrast, sep = "|")] + q <- r[, .(lo = quantile(est, 0.025), hi = quantile(est, 0.975)), by = .(world, key, variant)] + x <- merge(x, q, by = c("world", "key", "variant")) + dodge <- position_dodge(width = 0.6) + ggplot(x, aes(y = method, group = vlab)) + + geom_vline(aes(xintercept = 100 * ref), colour = "#0b0b0b", linewidth = 0.4) + + geom_vline(xintercept = 0, colour = "#c3c2b7", linewidth = 0.3, linetype = 2) + + geom_linerange(aes(xmin = 100 * lo, xmax = 100 * hi, colour = vlab), position = dodge, linewidth = 0.6) + + geom_point(aes(x = 100 * own), position = dodge, shape = 21, size = 2.6, fill = "white", colour = "#0b0b0b") + + geom_point(aes(x = 100 * mean_est, colour = vlab), position = dodge, size = 2) + + facet_wrap(~wlab, ncol = 4) + scale_colour_manual(values = VCOL, name = NULL) + + labs(x = "5-year risk difference, early − delayed (percentage points)", y = NULL, + caption = paste("Filled dot: mean estimate (bar: 2.5th–97.5th percentile across replicates).", + "Hollow dot: the method's own truth. Solid line: the reference truth (CCW A vs B1).")) + + coord_cartesian(xlim = c(-15, 35)) +} +ladder("harm") + ggtitle("Figure 1a. Estimand ladder, harm scenario (disease composite)") +``` + +```{r fig1b, fig.width = 11, fig.height = 9} +ladder("null") + ggtitle("Figure 1b. Estimand ladder, null scenario (disease composite)") +``` + +# 2. Aim 2: surgery-specific threats + +```{r tab3} +x <- perf[scenario == "harm" & outcome == "disease" & analysis == "main" & + variant %in% c("V2_cluster", "V3_random", "V4_within") & key != "ccw|A_B2"] +x[, cell := sprintf("%s (%s)", pp(bias), pp(mcse_bias))] +t3 <- dcast(x, method + vlab ~ wlab, value.var = "cell") +setnames(t3, c("method", "vlab"), c("Method", "Physician variant")) +knitr::kable(t3[order(-as.integer(Method))], caption = paste( + "Table 3. Estimation bias (MCSE) against each method's own truth, percentage points.", + "Harm scenario, disease composite.")) +``` + +```{r tab3b} +x[, cell2 := pp(total)] +t3b <- dcast(x, method + vlab ~ wlab, value.var = "cell2") +setnames(t3b, c("method", "vlab"), c("Method", "Physician variant")) +knitr::kable(t3b[order(-as.integer(Method))], caption = paste( + "Table 3b. Total error against the reference truth (CCW A vs B1), percentage points.", + "Harm scenario, disease composite.")) +``` + +**What it shows.** + +* **Surgeon skill (W1).** CCW and sequential trials stay within about 1 pp, and the FE variant brings + them to 0.2–0.3 pp. The **landmark is badly off (total error about 14–16 pp)**. Almost all of that is + question gap, not estimation bias, and Section 6 (Q1) explains why. +* **Practice style (W2).** It only harms the **naive** design (bias −9 to −12 pp). The TTE designs + absorb style through their time-varying PVI models. +* **Style that also changes the outcome coding (W3).** It biases landmark and sequential trials by + about 1.5 pp when the physician is ignored. A random intercept or FE removes most of it, which + supports H2b. +* **Deferral / crossover (W4).** No design bias. The TTE designs handle crossover by design. +* **Unmeasured severity (W5).** It biases **every** observational design by about 2.5–5.5 pp, and no + physician variant helps (H2c holds). +* **Realistic world (WR).** CCW is biased by +6 to +7 pp and sequential trials by +7 to +9 pp. FE helps + only a little. Unmeasured severity dominates. + +# 3. False alarms under the null (headline for clinicians) + +```{r fig2, fig.width = 10, fig.height = 5.2} +fa <- perf[scenario == "null" & outcome == "disease" & analysis == "main" & key != "ccw|A_B2" & variant != "V1_ignore"] +fa[, row := factor(paste0(method, " · ", sub(" \\(.*", "", vlab)))] +fa[, row := factor(row, levels = rev(sort(levels(row))))] +p1 <- ggplot(fa, aes(wlab, row, fill = 100 * reject0)) + geom_tile(colour = "white", linewidth = 1) + + geom_text(aes(label = sprintf("%.0f", 100 * reject0), colour = reject0 > 0.5), size = 3) + + scale_colour_manual(values = c(`TRUE` = "white", `FALSE` = "#0b0b0b"), guide = "none") + + scale_fill_gradient(low = "#eef4fc", high = "#0d3d78", limits = c(0, 100), name = "% of replicates\nCI excludes 0") + + labs(x = NULL, y = NULL, title = "Figure 2. False-alarm rate, null scenario (disease composite; nominal 5%)") + + theme(axis.text.x = element_text(angle = 30, hjust = 1), panel.grid = element_blank(), legend.position = "right") +p1 +``` + +```{r fig2b, fig.width = 8, fig.height = 3} +m0 <- perf[scenario == "null" & world == "W0" & variant %in% c("V2_cluster", "none") & + outcome %in% c("male", "disease") & !grepl("as_treated", key) & key != "ccw|A_B2"] +m0[, oc := factor(ifelse(outcome == "male", "MALE", "Disease composite"), levels = c("Disease composite", "MALE"))] +ggplot(m0, aes(100 * mean_est, method, colour = oc, group = oc)) + + geom_vline(xintercept = 0, colour = "#c3c2b7") + + geom_errorbarh(aes(xmin = 100 * (mean_est - 1.96 * emp_se), xmax = 100 * (mean_est + 1.96 * emp_se)), height = 0, linewidth = 0.6, position = position_dodge(width = 0.5)) + + geom_point(size = 2, position = position_dodge(width = 0.5)) + scale_colour_manual(values = c("Disease composite" = "#2a78d6", MALE = "#eb6834"), name = NULL) + + labs(x = "Risk difference (pp), W0 null", y = NULL, + title = "Figure 2 inset. Sharp null: disease 0, yet MALE about +14 pp") +``` + +**What it shows.** + +* In the clean, skill and deferral worlds, landmark, sequential trials and naive are near 5%. **CCW + is at 10–15% in W0/W1**, mainly because its SE is slightly underestimated in the null scenario + (model/empirical SE 0.86 in W0) and its estimate sits slightly low (−0.33 pp, 1.3 MCSE). The + bootstrap check (D28) is meant to settle this. +* Style that changes outcome coding (W3) pushes naive to 51–55% and sequential trials to 25% when the + physician is ignored. A random intercept or FE brings that back to 8–16%. +* Unmeasured severity (W5, WR) gives **80–100% false alarms** for landmark, sequential trials and + naive. CCW stays at 15–24%, but only because its SE is wide. FE lowers the landmark and sequential + rates in WR (31% and 60%). +* **MALE under the sharp null:** +14 pp for CCW, purely because only treated patients can have a + repeat revascularization. Every design "detects harm" on MALE in 93–100% of replicates. + +# 4. Aim 3: do negative controls detect bias? + +```{r tab4} +s <- ncs[level == "cell"] +s[, cell := ifelse(is.na(sens), "–", sprintf("%.0f%% / %s", 100 * sens, ifelse(is.na(spec), "–", sprintf("%.0f%%", 100 * spec))))] +s[, control := factor(c(nco1 = "NCO-1 (hip fracture / pneumonia)", nco2 = "NCO-2 (vein ablation)", + nce = "NCE (early low-value procedure)", any_nc = "Any of the three")[control], + levels = c("NCO-1 (hip fracture / pneumonia)", "NCO-2 (vein ablation)", + "NCE (early low-value procedure)", "Any of the three"))] +s[, wl := factor(c(WLAB, all = "All worlds")[world], levels = c(WLAB[WORLDS], "All worlds"))] +knitr::kable(dcast(s, control ~ wl, value.var = "cell"), caption = paste( + "Table 4. Sensitivity / specificity of each negative control for MALE bias > 1 pp.", + "A cell (world × scenario × method × variant) counts as biased when its mean MALE bias exceeds 1 pp;", + "a replicate is flagged when the control's 95% CI excludes 0. '–': no biased (or no clean) cells in that world.")) +``` + +```{r fig3, fig.width = 10, fig.height = 4.5} +nc[, design_lab := factor(c(ccw = "CCW", seq = "Sequential", landmark = "Landmark", naive = "Naive")[design], + levels = c("CCW", "Sequential", "Landmark", "Naive"))] +nc[, flag := factor(ifelse(any_nc, "A negative control flagged", "All negative controls clean"), + levels = c("All negative controls clean", "A negative control flagged"))] +nc[, wlab := factor(WLAB[world], levels = WLAB[WORLDS])] +cb <- nc[, .(absbias = abs(mean(err))), by = .(world, wlab, scenario, design_lab, variant, flag)] +ggplot(cb, aes(wlab, 100 * absbias, colour = flag)) + + geom_point(position = position_jitterdodge(jitter.width = 0.15, dodge.width = 0.6), size = 1.4, alpha = 0.8) + + geom_hline(yintercept = 1, linetype = 2, colour = "#52514e") + + scale_colour_manual(values = c("All negative controls clean" = "#1baf7a", "A negative control flagged" = "#eb6834"), name = NULL) + + facet_wrap(~design_lab, nrow = 1) + + labs(x = NULL, y = "|mean MALE bias| (pp)", title = "Figure 3. How much bias sits behind a clean negative control") + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +``` + +**What it shows (provisional, see Q2).** + +* **NCO-1 almost never flags (sensitivity about 12%), even with unmeasured severity (W5).** In the + current DGP, NCO-1 depends on frailty, smoking and age but **not on claudication severity**, so it + cannot see the main source of bias. H3a fails, by construction. +* **NCO-2 is the working detector (48% sensitivity overall, 65–67% in WR).** It also fires when style + does no harm (specificity 26–45% in W2/W3), which supports H3b. +* **The NCE almost never flags (about 5–9%).** It is adjusted for early PVI (D36), and it shares style + and frailty, not severity. +* **Bias hides behind clean controls.** In WR, the cells with clean negative controls still have a + median |MALE bias| of 8.5 pp (H3c). + +# 5. Simulated RCTs + +```{r tabrct} +r <- perf[analysis == "rct" & outcome == "disease" & scenario != "null"] +r <- r[, .(World = wlab, Scenario = scenario, Trial = method, `Reference` = pp(ref), `Own ITT truth` = pp(own), + `Mean estimate` = pp(mean_est), `Empirical SE` = pp(emp_se), `Power (CI excludes 0)` = sprintf("%.0f%%", 100 * reject0))] +knitr::kable(r[order(World, Scenario, Trial)], caption = "Table 5. Simulated trials, disease composite (pp).") +``` + +* **Surgeon selection changes the answer.** In WR, a realistic trial recruits hospital-based, + top-third-skill, low-style surgeons. Its own ITT truth is **+2.1 pp under harm and −1.4 pp under + benefit-then-harm**, while the population effects are +6.9 and +7.7. A well-run trial answers a + question about selected surgeons (H1c). +* **Small trials are uninformative.** With n = 240 the SE is about 4 pp, giving 6–29% power against + effects of 5–8 pp. +* The ideal pragmatic RCT (n = 50,000) recovers its truth with an SE of about 0.3–0.6 pp. + +# 6. Open questions raised by the draft run + +**Q1. Landmark adjusts for severity at month 5, after the PVI.** As specified in D13, the landmark +propensity model uses covariates at diagnosis **and at month W−1**. For treated patients, month-5 +severity is already changed by the PVI (relief, then faster progression), so it is a mediator. +Conditioning on it gives extreme weights (effective treated sample about 110–140) and a large own-truth +shift when the harm varies between patients (W1: own truth about 20 pp vs a reference of 7 pp). +A check on 20 oracle replicates gave these landmark estimates: + +| World (harm) | Adjust for month-5 severity | Baseline covariates only | Reference | +|---|---|---|---| +| W0 | 8.6 (SD 3.4) | 5.6 (SD 1.4) | 5.1 | +| W1 | 14.6 (SD 4.3) | 8.1 (SD 1.6) | 7.2 | + +**Q2. NCO-1 does not depend on severity.** D32 says NCO-1 "shares the latent patient frailty, +severity and smoking", but the DGP's NCO-1 model has no severity term. That is why it misses the +W5/WR bias. + +**Also pending:** the physician-cluster bootstrap check of SEs (D28). Model-to-empirical SE ratios +are near 1 for CCW (0.96–1.07, except WR 0.87). They are 0.83–0.92 for landmark and sequential trials +in W0, and 0.58 for landmark in W1. + +# Supplement: full performance table + +```{r supp} +x <- perf[analysis == "main" & outcome %in% c("disease", "male")] +knitr::kable(x[order(outcome, scenario, world, method, variant), + .(outcome, scenario, world, method = as.character(method), variant, est = pp(mean_est, 2), own = pp(own, 2), + ref = pp(ref, 2), bias = pp(bias, 2), mcse = pp(mcse_bias, 2), gap = pp(gap, 2), emp_se = pp(emp_se, 2), + se_ratio = round(se_ratio, 2), cover = round(cover_own, 2), reject0 = round(reject0, 2))]) +``` diff --git a/reports/draft-report.html b/reports/draft-report.html new file mode 100644 index 0000000..fe715bf --- /dev/null +++ b/reports/draft-report.html @@ -0,0 +1,19479 @@ + + + + + + + + + + + + + + +Early vs delayed PVI for claudication: known-truth simulation, draft results + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Read this first

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  • Run. 8 worlds × 3 truth scenarios × 100 replicates +of 50,000 patients (about 1,500 physicians per replicate). Each +replicate runs naive, landmark, CCW and sequential trials under 4 +physician variants, 3 negative controls, an oracle run, and 2 simulated +RCTs. 2,400 replicate files, 0 errors. 16 of 288,000 estimates are +missing (sequential trials with physician fixed effects, 4 replicates), +and these are dropped.
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  • Outcome shown. The headline outcome is the +disease composite (CLTI or major amputation), whose +true effect under the null is exactly 0 (D34). MALE is shown where it +adds something.
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  • Two errors, kept separate (D27). Estimation +bias = mean estimate − the method’s own truth. Question +gap = the method’s own truth − the reference truth (CCW A vs B1). +Total error is the sum.
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  • Variants shown. “Ignore physician” is V1’s point +estimate with the cluster-robust SE (V2). The V1 patient-level SE is in +the supplement tables.
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  • Two open questions came out of this run (Section +6). They affect the landmark results and the negative-control results, +so treat those parts as provisional.
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1. Aim 1: what each method answers (clean world W0)

+ + +++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Table 2. Disease composite, 5-year risk difference in +percentage points, world W0.
ScenarioMethodReference truthOwn truthQuestion gapMean estimateEstimation bias (MCSE)Empirical SECoverage of own truth
nullCCW, A vs B1 (reference)0.00.00.0-0.3-0.33 (0.26)2.685%
nullCCW, A vs B20.00.00.0-0.3-0.33 (0.26)2.685%
nullSequential trials, ITT0.00.00.0-0.0-0.02 (0.10)1.094%
nullLandmark at 6 months0.0-0.3-0.3-0.20.14 (0.06)1.097%
nullNaive ‘ever early PVI’0.01.31.31.1-0.20 (0.06)1.295%
nullRealistic RCT (n = 240), ITT0.00.00.0-0.1-0.07 (0.39)3.994%
nullIdeal pragmatic RCT, ITT0.00.00.0-0.1-0.05 (0.03)0.395%
harmCCW, A vs B1 (reference)5.15.10.05.30.17 (0.28)2.896%
harmCCW, A vs B25.15.20.05.30.17 (0.28)2.896%
harmSequential trials, ITT5.15.70.65.7-0.01 (0.15)1.591%
harmLandmark at 6 months5.17.92.87.8-0.10 (0.16)3.692%
harmNaive ‘ever early PVI’5.17.22.16.9-0.29 (0.06)1.688%
harmRealistic RCT (n = 240), ITT5.15.30.25.50.21 (0.43)4.397%
harmIdeal pragmatic RCT, ITT5.15.20.05.20.04 (0.03)0.395%
benefit_harmCCW, A vs B1 (reference)3.13.10.03.30.28 (0.29)2.989%
benefit_harmCCW, A vs B23.13.0-0.03.30.28 (0.29)2.989%
benefit_harmSequential trials, ITT3.14.00.94.20.26 (0.11)1.199%
benefit_harmLandmark at 6 months3.15.32.35.40.03 (0.19)3.595%
benefit_harmNaive ‘ever early PVI’3.15.72.65.4-0.26 (0.07)1.297%
benefit_harmRealistic RCT (n = 240), ITT3.13.90.83.8-0.08 (0.43)4.394%
benefit_harmIdeal pragmatic RCT, ITT3.13.90.83.90.01 (0.03)0.392%
+

What it shows.

+
    +
  • Every TTE method is close to unbiased for its own +truth in W0 (all |bias| < 0.35 pp). H1a holds.
    • +
  • The methods answer different questions. Under harm +(reference +5.1 pp): sequential trials target +5.7, landmark +7.9 and +naive +7.2. So the question gap alone is 0.6–2.8 pp. Under the null, +naive has a gap of +1.3 pp from immortal time even with every confounder +measured. H1b holds, though the gap for the sequential trials is +small.
    • +
  • CCW is right but imprecise. Its empirical SE is +about 2.8 pp at n = 50,000, against about 1.5 pp for sequential trials. +Under the null its 95% CI excludes 0 in 15% of replicates (Section +3).
    • +
  • The realistic RCT (n = 240) is unbiased for its own +ITT, but its SE is about 4.2 pp. It detects the 5-point harm in only 25% +of replicates.
    • +
+

+

+
+
+

2. Aim 2: surgery-specific threats

+ + ++++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Table 3. Estimation bias (MCSE) against each method’s own +truth, percentage points. Harm scenario, disease composite.
MethodPhysician variantW0 CleanW1 SkillW1 Skill (rho 0)W2 StyleW3 Style -> outcomeW4 DeferralW5 Unmeasured severityWR Realistic
CCW, A vs B1 (reference)Ignore physician (cluster SE)0.2 (0.3)1.1 (0.3)0.2 (0.3)0.2 (0.3)0.7 (0.3)-0.0 (0.3)4.4 (0.2)7.1 (0.4)
CCW, A vs B1 (reference)Random intercept0.2 (0.3)0.9 (0.3)0.4 (0.3)0.1 (0.4)0.1 (0.4)0.0 (0.3)4.4 (0.3)6.4 (0.4)
CCW, A vs B1 (reference)Within-physician (FE)0.1 (0.4)0.3 (0.4)0.2 (0.4)-0.2 (0.5)-0.3 (0.4)-0.1 (0.3)4.4 (0.3)6.1 (0.4)
Sequential trials, ITTIgnore physician (cluster SE)-0.0 (0.1)1.2 (0.1)0.1 (0.1)0.2 (0.1)1.4 (0.1)-0.1 (0.1)4.9 (0.1)9.3 (0.2)
Sequential trials, ITTRandom intercept-0.0 (0.2)0.9 (0.1)0.1 (0.1)0.0 (0.2)0.2 (0.2)-0.2 (0.1)5.0 (0.1)7.4 (0.2)
Sequential trials, ITTWithin-physician (FE)-0.3 (0.2)0.2 (0.2)-0.1 (0.2)-0.3 (0.2)-0.5 (0.2)-0.5 (0.1)4.9 (0.1)6.7 (0.2)
Landmark at 6 monthsIgnore physician (cluster SE)-0.1 (0.2)3.9 (0.4)1.1 (0.3)0.1 (0.8)1.5 (0.7)-0.0 (0.2)2.5 (0.4)1.2 (1.1)
Landmark at 6 monthsRandom intercept-0.2 (0.2)2.7 (0.3)0.8 (0.3)-0.3 (0.7)0.3 (0.5)0.0 (0.1)2.6 (0.4)-0.5 (1.1)
Landmark at 6 monthsWithin-physician (FE)-0.7 (0.3)1.5 (0.5)0.8 (0.5)-1.1 (0.7)-1.0 (0.6)-0.4 (0.3)2.5 (0.4)-1.3 (1.1)
Naive ‘ever early PVI’Ignore physician (cluster SE)-0.3 (0.1)0.5 (0.1)-0.3 (0.1)-10.5 (1.0)-6.4 (0.9)-0.2 (0.1)5.5 (0.1)1.5 (1.0)
Naive ‘ever early PVI’Random intercept-0.2 (0.1)0.4 (0.1)-0.2 (0.1)-9.0 (0.9)-6.1 (0.9)-0.1 (0.1)5.6 (0.1)0.6 (1.0)
Naive ‘ever early PVI’Within-physician (FE)-0.9 (0.1)-0.7 (0.1)-0.8 (0.1)-11.5 (1.0)-8.9 (0.9)-1.0 (0.1)5.3 (0.1)-1.0 (1.0)
+ + ++++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Table 3b. Total error against the reference truth (CCW A vs +B1), percentage points. Harm scenario, disease composite.
MethodPhysician variantW0 CleanW1 SkillW1 Skill (rho 0)W2 StyleW3 Style -> outcomeW4 DeferralW5 Unmeasured severityWR Realistic
CCW, A vs B1 (reference)Ignore physician (cluster SE)0.21.10.20.20.7-0.04.47.1
CCW, A vs B1 (reference)Random intercept0.20.90.40.10.10.04.46.4
CCW, A vs B1 (reference)Within-physician (FE)0.10.30.2-0.2-0.3-0.14.46.1
Sequential trials, ITTIgnore physician (cluster SE)0.62.00.90.82.00.55.510.0
Sequential trials, ITTRandom intercept0.61.70.90.60.80.55.68.2
Sequential trials, ITTWithin-physician (FE)0.31.00.70.30.10.15.57.4
Landmark at 6 monthsIgnore physician (cluster SE)2.716.514.13.23.52.45.69.8
Landmark at 6 monthsRandom intercept2.715.413.72.92.22.45.68.1
Landmark at 6 monthsWithin-physician (FE)2.214.213.72.01.02.05.57.3
Naive ‘ever early PVI’Ignore physician (cluster SE)1.83.12.12.13.21.77.611.8
Naive ‘ever early PVI’Random intercept1.93.02.23.63.51.87.710.9
Naive ‘ever early PVI’Within-physician (FE)1.21.91.61.10.70.97.49.3
+

What it shows.

+
    +
  • Surgeon skill (W1). CCW and sequential trials stay +within about 1 pp, and the FE variant brings them to 0.2–0.3 pp. The +landmark is badly off (total error about 14–16 pp). +Almost all of that is question gap, not estimation bias, and Section 6 +(Q1) explains why.
    • +
  • Practice style (W2). It only harms the +naive design (bias −9 to −12 pp). The TTE designs +absorb style through their time-varying PVI models.
    • +
  • Style that also changes the outcome coding (W3). It +biases landmark and sequential trials by about 1.5 pp when the physician +is ignored. A random intercept or FE removes most of it, which supports +H2b.
    • +
  • Deferral / crossover (W4). No design bias. The TTE +designs handle crossover by design.
    • +
  • Unmeasured severity (W5). It biases +every observational design by about 2.5–5.5 pp, and no +physician variant helps (H2c holds).
    • +
  • Realistic world (WR). CCW is biased by +6 to +7 pp +and sequential trials by +7 to +9 pp. FE helps only a little. Unmeasured +severity dominates.
    • +
+
+
+

3. False alarms under the null (headline for clinicians)

+

+

+

What it shows.

+
    +
  • In the clean, skill and deferral worlds, landmark, sequential trials +and naive are near 5%. CCW is at 10–15% in W0/W1, +mainly because its SE is slightly underestimated in the null scenario +(model/empirical SE 0.86 in W0) and its estimate sits slightly low +(−0.33 pp, 1.3 MCSE). The bootstrap check (D28) is meant to settle +this.
    • +
  • Style that changes outcome coding (W3) pushes naive to 51–55% and +sequential trials to 25% when the physician is ignored. A random +intercept or FE brings that back to 8–16%.
    • +
  • Unmeasured severity (W5, WR) gives 80–100% false +alarms for landmark, sequential trials and naive. CCW stays at +15–24%, but only because its SE is wide. FE lowers the landmark and +sequential rates in WR (31% and 60%).
    • +
  • MALE under the sharp null: +14 pp for CCW, purely +because only treated patients can have a repeat revascularization. Every +design “detects harm” on MALE in 93–100% of replicates.
    • +
+
+
+

4. Aim 3: do negative controls detect bias?

+ + ++++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Table 4. Sensitivity / specificity of each negative control for +MALE bias > 1 pp. A cell (world × scenario × method × variant) counts +as biased when its mean MALE bias exceeds 1 pp; a replicate is flagged +when the control’s 95% CI excludes 0. ‘–’: no biased (or no clean) cells +in that world.
controlW0 CleanW1 SkillW1 Skill (rho 0)W2 StyleW3 Style -> outcomeW4 DeferralW5 Unmeasured severityWR RealisticAll worlds
NCO-1 (hip fracture / pneumonia)–11% / 94%19% / 89%13% / 87%13% / 89%12% / 92%10% / –12% / 88%12% / 90%
NCO-2 (vein ablation)–25% / 78%7% / 75%54% / 45%52% / 26%11% / 74%42% / –67% / 92%48% / 70%
NCE (early low-value procedure)–4% / 94%6% / 96%5% / 95%9% / 88%7% / 94%6% / –9% / 93%7% / 94%
Any of the three–37% / 72%30% / 65%63% / 36%61% / 20%25% / 65%50% / –73% / 78%57% / 61%
+

+

What it shows (provisional, see Q2).

+
    +
  • NCO-1 almost never flags (sensitivity about 12%), even with +unmeasured severity (W5). In the current DGP, NCO-1 depends on +frailty, smoking and age but not on claudication +severity, so it cannot see the main source of bias. H3a fails, +by construction.
    • +
  • NCO-2 is the working detector (48% sensitivity overall, +65–67% in WR). It also fires when style does no harm +(specificity 26–45% in W2/W3), which supports H3b.
    • +
  • The NCE almost never flags (about 5–9%). It is +adjusted for early PVI (D36), and it shares style and frailty, not +severity.
    • +
  • Bias hides behind clean controls. In WR, the cells +with clean negative controls still have a median |MALE bias| of 8.5 pp +(H3c).
    • +
+
+
+

5. Simulated RCTs

+ + ++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Table 5. Simulated trials, disease composite (pp).
WorldScenarioTrialReferenceOwn ITT truthMean estimateEmpirical SEPower (CI excludes 0)
W0 Cleanbenefit_harmIdeal RCT, as-treated3.1NA4.90.3100%
W0 Cleanbenefit_harmRealistic RCT, as-treated3.1NA4.74.119%
W0 Cleanbenefit_harmIdeal pragmatic RCT, ITT3.13.93.90.3100%
W0 Cleanbenefit_harmRealistic RCT (n = 240), ITT3.13.93.84.313%
W0 CleanharmIdeal RCT, as-treated5.1NA6.50.3100%
W0 CleanharmRealistic RCT, as-treated5.1NA6.74.029%
W0 CleanharmIdeal pragmatic RCT, ITT5.15.25.20.3100%
W0 CleanharmRealistic RCT (n = 240), ITT5.15.35.54.325%
W1 Skillbenefit_harmIdeal RCT, as-treated7.9NA10.70.8100%
W1 Skillbenefit_harmRealistic RCT, as-treated7.9NA-2.04.010%
W1 Skillbenefit_harmIdeal pragmatic RCT, ITT7.98.78.70.7100%
W1 Skillbenefit_harmRealistic RCT (n = 240), ITT7.9-1.4-2.14.09%
W1 SkillharmIdeal RCT, as-treated7.2NA9.10.5100%
W1 SkillharmRealistic RCT, as-treated7.2NA2.34.46%
W1 SkillharmIdeal pragmatic RCT, ITT7.27.37.30.4100%
W1 SkillharmRealistic RCT (n = 240), ITT7.22.12.04.58%
W1 Skill (rho 0)benefit_harmIdeal RCT, as-treated7.9NA10.70.7100%
W1 Skill (rho 0)benefit_harmRealistic RCT, as-treated7.9NA-1.73.49%
W1 Skill (rho 0)benefit_harmIdeal pragmatic RCT, ITT7.98.88.80.6100%
W1 Skill (rho 0)benefit_harmRealistic RCT (n = 240), ITT7.9-1.1-1.63.46%
W1 Skill (rho 0)harmIdeal RCT, as-treated7.1NA8.90.4100%
W1 Skill (rho 0)harmRealistic RCT, as-treated7.1NA2.14.710%
W1 Skill (rho 0)harmIdeal pragmatic RCT, ITT7.17.37.30.4100%
W1 Skill (rho 0)harmRealistic RCT (n = 240), ITT7.12.31.84.712%
W2 Stylebenefit_harmIdeal RCT, as-treated3.0NA4.90.3100%
W2 Stylebenefit_harmRealistic RCT, as-treated3.0NA4.84.518%
W2 Stylebenefit_harmIdeal pragmatic RCT, ITT3.03.94.00.3100%
W2 Stylebenefit_harmRealistic RCT (n = 240), ITT3.03.94.34.318%
W2 StyleharmIdeal RCT, as-treated5.1NA6.40.3100%
W2 StyleharmRealistic RCT, as-treated5.1NA5.94.625%
W2 StyleharmIdeal pragmatic RCT, ITT5.15.25.20.3100%
W2 StyleharmRealistic RCT (n = 240), ITT5.15.45.34.522%
W3 Style -> outcomebenefit_harmIdeal RCT, as-treated3.0NA4.80.3100%
W3 Style -> outcomebenefit_harmRealistic RCT, as-treated3.0NA4.84.426%
W3 Style -> outcomebenefit_harmIdeal pragmatic RCT, ITT3.03.83.90.3100%
W3 Style -> outcomebenefit_harmRealistic RCT (n = 240), ITT3.03.94.04.015%
W3 Style -> outcomeharmIdeal RCT, as-treated5.0NA6.30.3100%
W3 Style -> outcomeharmRealistic RCT, as-treated5.0NA6.04.631%
W3 Style -> outcomeharmIdeal pragmatic RCT, ITT5.05.05.10.3100%
W3 Style -> outcomeharmRealistic RCT (n = 240), ITT5.05.35.94.428%
W4 Deferralbenefit_harmIdeal RCT, as-treated3.3NA5.10.3100%
W4 Deferralbenefit_harmRealistic RCT, as-treated3.3NA4.64.817%
W4 Deferralbenefit_harmIdeal pragmatic RCT, ITT3.34.14.10.3100%
W4 Deferralbenefit_harmRealistic RCT (n = 240), ITT3.34.04.14.717%
W4 DeferralharmIdeal RCT, as-treated4.8NA6.20.3100%
W4 DeferralharmRealistic RCT, as-treated4.8NA6.44.325%
W4 DeferralharmIdeal pragmatic RCT, ITT4.84.94.90.3100%
W4 DeferralharmRealistic RCT (n = 240), ITT4.85.25.33.919%
W5 Unmeasured severitybenefit_harmIdeal RCT, as-treated3.1NA4.90.3100%
W5 Unmeasured severitybenefit_harmRealistic RCT, as-treated3.1NA4.64.318%
W5 Unmeasured severitybenefit_harmIdeal pragmatic RCT, ITT3.13.93.90.3100%
W5 Unmeasured severitybenefit_harmRealistic RCT (n = 240), ITT3.13.93.94.313%
W5 Unmeasured severityharmIdeal RCT, as-treated5.1NA6.40.4100%
W5 Unmeasured severityharmRealistic RCT, as-treated5.1NA6.24.829%
W5 Unmeasured severityharmIdeal pragmatic RCT, ITT5.15.25.10.3100%
W5 Unmeasured severityharmRealistic RCT (n = 240), ITT5.15.35.94.733%
WR Realisticbenefit_harmIdeal RCT, as-treated7.7NA10.60.8100%
WR Realisticbenefit_harmRealistic RCT, as-treated7.7NA-1.63.78%
WR Realisticbenefit_harmIdeal pragmatic RCT, ITT7.78.48.40.6100%
WR Realisticbenefit_harmRealistic RCT (n = 240), ITT7.7-1.4-1.33.76%
WR RealisticharmIdeal RCT, as-treated6.9NA8.80.5100%
WR RealisticharmRealistic RCT, as-treated6.9NA1.84.411%
WR RealisticharmIdeal pragmatic RCT, ITT6.97.07.00.4100%
WR RealisticharmRealistic RCT (n = 240), ITT6.92.11.74.29%
+
    +
  • Surgeon selection changes the answer. In WR, a +realistic trial recruits hospital-based, top-third-skill, low-style +surgeons. Its own ITT truth is +2.1 pp under harm and −1.4 pp +under benefit-then-harm, while the population effects are +6.9 +and +7.7. A well-run trial answers a question about selected surgeons +(H1c).
    • +
  • Small trials are uninformative. With n = 240 the SE +is about 4 pp, giving 6–29% power against effects of 5–8 pp.
    • +
  • The ideal pragmatic RCT (n = 50,000) recovers its truth with an SE +of about 0.3–0.6 pp.
    • +
+
+
+

6. Open questions raised by the draft run

+

Q1. Landmark adjusts for severity at month 5, after the +PVI. As specified in D13, the landmark propensity model uses +covariates at diagnosis and at month W−1. For treated +patients, month-5 severity is already changed by the PVI (relief, then +faster progression), so it is a mediator. Conditioning on it gives +extreme weights (effective treated sample about 110–140) and a large +own-truth shift when the harm varies between patients (W1: own truth +about 20 pp vs a reference of 7 pp). A check on 20 oracle replicates +gave these landmark estimates:

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + +
World (harm)Adjust for month-5 severityBaseline covariates onlyReference
W08.6 (SD 3.4)5.6 (SD 1.4)5.1
W114.6 (SD 4.3)8.1 (SD 1.6)7.2
+

Q2. NCO-1 does not depend on severity. D32 says +NCO-1 “shares the latent patient frailty, severity and smoking”, but the +DGP’s NCO-1 model has no severity term. That is why it misses the W5/WR +bias.

+

Also pending: the physician-cluster bootstrap check +of SEs (D28). Model-to-empirical SE ratios are near 1 for CCW +(0.96–1.07, except WR 0.87). They are 0.83–0.92 for landmark and +sequential trials in W0, and 0.58 for landmark in W1.

+
+
+

Supplement: full performance table

+ +++++++++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
outcomescenarioworldmethodvariantestownrefbiasmcsegapemp_sese_ratiocoverreject0
diseasebenefit_harmW0Naive ‘ever early PVI’V1_ignore5.435.693.05-0.260.072.641.221.150.971.00
diseasebenefit_harmW0Naive ‘ever early PVI’V2_cluster5.435.693.05-0.260.072.641.221.150.971.00
diseasebenefit_harmW0Naive ‘ever early PVI’V3_random5.555.693.05-0.140.072.641.291.110.961.00
diseasebenefit_harmW0Naive ‘ever early PVI’V4_within4.925.693.05-0.780.132.641.800.950.840.89
diseasebenefit_harmW0Landmark at 6 monthsV1_ignore5.385.353.050.030.192.303.521.100.950.24
diseasebenefit_harmW0Landmark at 6 monthsV2_cluster5.385.353.050.030.192.303.521.100.950.24
diseasebenefit_harmW0Landmark at 6 monthsV3_random5.385.353.050.030.192.303.551.090.950.24
diseasebenefit_harmW0Landmark at 6 monthsV4_within5.875.353.050.520.452.306.470.830.900.14
diseasebenefit_harmW0Sequential trials, ITTV1_ignore4.253.983.050.260.110.931.071.150.990.96
diseasebenefit_harmW0Sequential trials, ITTV2_cluster4.253.983.050.260.110.931.071.150.990.96
diseasebenefit_harmW0Sequential trials, ITTV3_random4.223.983.050.240.110.931.131.100.990.96
diseasebenefit_harmW0Sequential trials, ITTV4_within4.053.983.050.070.170.931.700.870.890.79
diseasebenefit_harmW0CCW, A vs B2V1_ignore3.313.033.050.280.29-0.022.880.930.870.20
diseasebenefit_harmW0CCW, A vs B2V2_cluster3.313.033.050.280.29-0.022.880.930.890.18
diseasebenefit_harmW0CCW, A vs B2V3_random3.363.033.050.330.28-0.022.850.940.900.19
diseasebenefit_harmW0CCW, A vs B2V4_within3.283.033.050.250.31-0.023.120.990.920.15
diseasebenefit_harmW0CCW, A vs B1 (reference)V1_ignore3.333.053.050.280.290.002.880.930.870.20
diseasebenefit_harmW0CCW, A vs B1 (reference)V2_cluster3.333.053.050.280.290.002.880.920.890.18
diseasebenefit_harmW0CCW, A vs B1 (reference)V3_random3.383.053.050.330.280.002.850.940.910.19
diseasebenefit_harmW0CCW, A vs B1 (reference)V4_within3.293.053.050.240.310.003.120.990.920.15
diseasebenefit_harmW1Naive ‘ever early PVI’V1_ignore12.2510.687.941.570.102.731.690.940.831.00
diseasebenefit_harmW1Naive ‘ever early PVI’V2_cluster12.2510.687.941.570.102.731.691.040.911.00
diseasebenefit_harmW1Naive ‘ever early PVI’V3_random11.8410.687.941.160.082.731.631.050.921.00
diseasebenefit_harmW1Naive ‘ever early PVI’V4_within10.5110.687.94-0.170.122.731.871.060.981.00
diseasebenefit_harmW1Landmark at 6 monthsV1_ignore11.968.867.943.100.240.915.330.820.850.82
diseasebenefit_harmW1Landmark at 6 monthsV2_cluster11.968.867.943.100.240.915.330.840.860.81
diseasebenefit_harmW1Landmark at 6 monthsV3_random11.718.867.942.850.230.915.260.840.870.81
diseasebenefit_harmW1Landmark at 6 monthsV4_within10.138.867.941.270.440.916.850.760.850.54
diseasebenefit_harmW1Sequential trials, ITTV1_ignore11.229.267.941.970.161.311.630.900.731.00
diseasebenefit_harmW1Sequential trials, ITTV2_cluster11.229.267.941.970.161.311.631.020.831.00
diseasebenefit_harmW1Sequential trials, ITTV3_random10.679.267.941.410.161.311.551.040.901.00
diseasebenefit_harmW1Sequential trials, ITTV4_within9.789.267.940.520.171.311.741.050.971.00
diseasebenefit_harmW1CCW, A vs B2V1_ignore9.637.997.941.640.370.043.720.870.900.89
diseasebenefit_harmW1CCW, A vs B2V2_cluster9.637.997.941.640.370.043.720.890.910.89
diseasebenefit_harmW1CCW, A vs B2V3_random9.177.997.941.180.370.043.700.870.930.86
diseasebenefit_harmW1CCW, A vs B2V4_within8.527.997.940.530.430.044.340.840.870.66
diseasebenefit_harmW1CCW, A vs B1 (reference)V1_ignore9.587.947.941.640.370.003.720.870.900.88
diseasebenefit_harmW1CCW, A vs B1 (reference)V2_cluster9.587.947.941.640.370.003.720.890.920.89
diseasebenefit_harmW1CCW, A vs B1 (reference)V3_random9.127.947.941.180.370.003.700.870.930.86
diseasebenefit_harmW1CCW, A vs B1 (reference)V4_within8.467.947.940.520.430.004.340.840.870.65
diseasebenefit_harmW1_rho0Naive ‘ever early PVI’V1_ignore10.4810.727.92-0.230.092.801.670.920.941.00
diseasebenefit_harmW1_rho0Naive ‘ever early PVI’V2_cluster10.4810.727.92-0.230.092.801.671.000.951.00
diseasebenefit_harmW1_rho0Naive ‘ever early PVI’V3_random10.6210.727.92-0.090.082.801.740.970.951.00
diseasebenefit_harmW1_rho0Naive ‘ever early PVI’V4_within9.9810.727.92-0.740.122.802.220.930.881.00
diseasebenefit_harmW1_rho0Landmark at 6 monthsV1_ignore11.0910.127.920.970.242.215.200.890.950.79
diseasebenefit_harmW1_rho0Landmark at 6 monthsV2_cluster11.0910.127.920.970.242.215.200.910.950.79
diseasebenefit_harmW1_rho0Landmark at 6 monthsV3_random11.0310.127.920.910.232.215.190.900.940.79
diseasebenefit_harmW1_rho0Landmark at 6 monthsV4_within10.2210.127.920.100.582.217.210.850.820.46
diseasebenefit_harmW1_rho0Sequential trials, ITTV1_ignore9.369.287.920.080.161.361.560.910.951.00
diseasebenefit_harmW1_rho0Sequential trials, ITTV2_cluster9.369.287.920.080.161.361.561.000.951.00
diseasebenefit_harmW1_rho0Sequential trials, ITTV3_random9.369.287.920.080.161.361.630.960.951.00
diseasebenefit_harmW1_rho0Sequential trials, ITTV4_within9.149.287.92-0.140.211.362.070.900.921.00
diseasebenefit_harmW1_rho0CCW, A vs B2V1_ignore7.817.947.92-0.130.300.023.041.010.950.82
diseasebenefit_harmW1_rho0CCW, A vs B2V2_cluster7.817.947.92-0.130.300.023.041.020.950.80
diseasebenefit_harmW1_rho0CCW, A vs B2V3_random7.917.947.92-0.030.330.023.340.950.930.76
diseasebenefit_harmW1_rho0CCW, A vs B2V4_within8.057.947.920.110.450.024.470.860.910.60
diseasebenefit_harmW1_rho0CCW, A vs B1 (reference)V1_ignore7.787.927.92-0.140.300.003.041.010.950.79
diseasebenefit_harmW1_rho0CCW, A vs B1 (reference)V2_cluster7.787.927.92-0.140.300.003.041.020.950.79
diseasebenefit_harmW1_rho0CCW, A vs B1 (reference)V3_random7.887.927.92-0.040.330.003.340.950.930.75
diseasebenefit_harmW1_rho0CCW, A vs B1 (reference)V4_within8.037.927.920.110.450.004.470.860.910.60
diseasebenefit_harmW2Naive ‘ever early PVI’V1_ignore5.7014.363.05-8.660.9911.311.371.030.001.00
diseasebenefit_harmW2Naive ‘ever early PVI’V2_cluster5.7014.363.05-8.660.9911.311.371.030.001.00
diseasebenefit_harmW2Naive ‘ever early PVI’V3_random6.9814.363.05-7.380.9611.312.111.140.150.94
diseasebenefit_harmW2Naive ‘ever early PVI’V4_within4.6514.363.05-9.711.0311.312.401.050.060.54
diseasebenefit_harmW2Landmark at 6 monthsV1_ignore4.884.293.050.590.831.243.491.060.940.18
diseasebenefit_harmW2Landmark at 6 monthsV2_cluster4.884.293.050.590.831.243.491.050.950.18
diseasebenefit_harmW2Landmark at 6 monthsV3_random5.234.293.050.940.811.243.841.020.970.16
diseasebenefit_harmW2Landmark at 6 monthsV4_within4.954.293.050.660.881.245.550.960.910.05
diseasebenefit_harmW2Sequential trials, ITTV1_ignore4.393.983.050.410.130.931.280.960.920.98
diseasebenefit_harmW2Sequential trials, ITTV2_cluster4.393.983.050.410.130.931.280.960.910.99
diseasebenefit_harmW2Sequential trials, ITTV3_random4.153.983.050.180.170.931.651.020.970.74
diseasebenefit_harmW2Sequential trials, ITTV4_within3.883.983.05-0.100.190.931.861.010.940.49
diseasebenefit_harmW2CCW, A vs B2V1_ignore3.133.033.050.090.25-0.022.451.130.960.11
diseasebenefit_harmW2CCW, A vs B2V2_cluster3.133.033.050.090.25-0.022.451.120.970.14
diseasebenefit_harmW2CCW, A vs B2V3_random3.503.033.050.470.32-0.023.161.140.920.07
diseasebenefit_harmW2CCW, A vs B2V4_within3.443.033.050.410.40-0.023.951.050.900.05
diseasebenefit_harmW2CCW, A vs B1 (reference)V1_ignore3.153.053.050.100.250.002.451.130.960.11
diseasebenefit_harmW2CCW, A vs B1 (reference)V2_cluster3.153.053.050.100.250.002.451.120.970.14
diseasebenefit_harmW2CCW, A vs B1 (reference)V3_random3.523.053.050.470.320.003.161.140.920.07
diseasebenefit_harmW2CCW, A vs B1 (reference)V4_within3.443.053.050.390.400.003.971.050.900.05
diseasebenefit_harmW3Naive ‘ever early PVI’V1_ignore6.9214.672.98-7.750.9211.691.610.920.001.00
diseasebenefit_harmW3Naive ‘ever early PVI’V2_cluster6.9214.672.98-7.750.9211.691.610.910.001.00
diseasebenefit_harmW3Naive ‘ever early PVI’V3_random7.3814.672.98-7.290.8611.692.700.900.230.93
diseasebenefit_harmW3Naive ‘ever early PVI’V4_within4.6914.672.98-9.970.9211.692.780.930.080.50
diseasebenefit_harmW3Landmark at 6 monthsV1_ignore5.814.112.981.700.561.133.831.040.980.23
diseasebenefit_harmW3Landmark at 6 monthsV2_cluster5.814.112.981.700.561.133.831.040.980.26
diseasebenefit_harmW3Landmark at 6 monthsV3_random5.534.112.981.420.491.133.731.070.950.17
diseasebenefit_harmW3Landmark at 6 monthsV4_within4.544.112.980.430.591.135.280.920.890.11
diseasebenefit_harmW3Sequential trials, ITTV1_ignore5.523.882.981.640.140.901.410.920.741.00
diseasebenefit_harmW3Sequential trials, ITTV2_cluster5.523.882.981.640.140.901.410.910.741.00
diseasebenefit_harmW3Sequential trials, ITTV3_random4.443.882.980.560.190.901.940.900.950.75
diseasebenefit_harmW3Sequential trials, ITTV4_within3.853.882.98-0.030.230.902.250.860.900.52
diseasebenefit_harmW3CCW, A vs B2V1_ignore3.702.962.980.740.30-0.013.000.980.960.23
diseasebenefit_harmW3CCW, A vs B2V2_cluster3.702.962.980.740.30-0.013.000.990.960.24
diseasebenefit_harmW3CCW, A vs B2V3_random3.222.962.980.260.40-0.014.010.950.880.07
diseasebenefit_harmW3CCW, A vs B2V4_within2.832.962.98-0.130.47-0.014.740.900.820.06
diseasebenefit_harmW3CCW, A vs B1 (reference)V1_ignore3.712.982.980.730.300.003.000.980.960.22
diseasebenefit_harmW3CCW, A vs B1 (reference)V2_cluster3.712.982.980.730.300.003.000.990.960.23
diseasebenefit_harmW3CCW, A vs B1 (reference)V3_random3.232.982.980.250.400.004.020.950.880.07
diseasebenefit_harmW3CCW, A vs B1 (reference)V4_within2.872.982.98-0.110.470.004.750.900.820.07
diseasebenefit_harmW4Naive ‘ever early PVI’V1_ignore5.475.753.29-0.270.082.451.341.040.930.99
diseasebenefit_harmW4Naive ‘ever early PVI’V2_cluster5.475.753.29-0.270.082.451.341.040.920.99
diseasebenefit_harmW4Naive ‘ever early PVI’V3_random5.635.753.29-0.110.072.451.391.040.940.99
diseasebenefit_harmW4Naive ‘ever early PVI’V4_within5.015.753.29-0.730.132.451.631.060.880.92
diseasebenefit_harmW4Landmark at 6 monthsV1_ignore4.934.773.290.160.141.483.431.030.940.22
diseasebenefit_harmW4Landmark at 6 monthsV2_cluster4.934.773.290.160.141.483.431.030.950.23
diseasebenefit_harmW4Landmark at 6 monthsV3_random4.904.773.290.130.131.483.421.030.950.23
diseasebenefit_harmW4Landmark at 6 monthsV4_within4.104.773.29-0.670.281.484.420.970.820.09
diseasebenefit_harmW4Sequential trials, ITTV1_ignore4.324.223.290.100.120.931.191.040.940.94
diseasebenefit_harmW4Sequential trials, ITTV2_cluster4.324.223.290.100.120.931.191.040.940.95
diseasebenefit_harmW4Sequential trials, ITTV3_random4.344.223.290.130.120.931.241.020.940.95
diseasebenefit_harmW4Sequential trials, ITTV4_within4.204.223.29-0.020.150.931.540.970.930.85
diseasebenefit_harmW4CCW, A vs B2V1_ignore2.863.043.29-0.180.26-0.252.640.980.910.17
diseasebenefit_harmW4CCW, A vs B2V2_cluster2.863.043.29-0.180.26-0.252.640.970.910.17
diseasebenefit_harmW4CCW, A vs B2V3_random2.923.043.29-0.120.27-0.252.740.950.910.17
diseasebenefit_harmW4CCW, A vs B2V4_within2.653.043.29-0.390.32-0.253.200.920.870.13
diseasebenefit_harmW4CCW, A vs B1 (reference)V1_ignore3.123.293.29-0.170.260.002.640.980.910.18
diseasebenefit_harmW4CCW, A vs B1 (reference)V2_cluster3.123.293.29-0.170.260.002.640.970.910.18
diseasebenefit_harmW4CCW, A vs B1 (reference)V3_random3.143.293.29-0.160.270.002.730.950.910.20
diseasebenefit_harmW4CCW, A vs B1 (reference)V4_within2.913.293.29-0.380.320.003.190.920.870.14
diseasebenefit_harmW5Naive ‘ever early PVI’V1_ignore10.215.183.055.030.152.131.260.890.001.00
diseasebenefit_harmW5Naive ‘ever early PVI’V2_cluster10.215.183.055.030.152.131.260.890.011.00
diseasebenefit_harmW5Naive ‘ever early PVI’V3_random10.275.183.055.090.142.131.280.890.001.00
diseasebenefit_harmW5Naive ‘ever early PVI’V4_within10.025.183.054.840.152.131.470.940.061.00
diseasebenefit_harmW5Landmark at 6 monthsV1_ignore10.295.093.055.190.402.041.290.890.011.00
diseasebenefit_harmW5Landmark at 6 monthsV2_cluster10.295.093.055.190.402.041.290.890.011.00
diseasebenefit_harmW5Landmark at 6 monthsV3_random10.305.093.055.200.402.041.300.900.001.00
diseasebenefit_harmW5Landmark at 6 monthsV4_within10.325.093.055.220.412.041.540.920.041.00
diseasebenefit_harmW5Sequential trials, ITTV1_ignore8.373.983.054.390.120.931.220.910.001.00
diseasebenefit_harmW5Sequential trials, ITTV2_cluster8.373.983.054.390.120.931.220.910.001.00
diseasebenefit_harmW5Sequential trials, ITTV3_random8.343.983.054.360.130.931.260.900.011.00
diseasebenefit_harmW5Sequential trials, ITTV4_within8.293.983.054.310.150.931.470.920.121.00
diseasebenefit_harmW5CCW, A vs B2V1_ignore6.813.033.053.770.25-0.022.460.970.750.88
diseasebenefit_harmW5CCW, A vs B2V2_cluster6.813.033.053.770.25-0.022.460.970.740.88
diseasebenefit_harmW5CCW, A vs B2V3_random6.833.033.053.800.25-0.022.540.960.760.87
diseasebenefit_harmW5CCW, A vs B2V4_within6.863.033.053.830.30-0.023.030.960.820.71
diseasebenefit_harmW5CCW, A vs B1 (reference)V1_ignore6.833.053.053.780.250.002.460.970.750.89
diseasebenefit_harmW5CCW, A vs B1 (reference)V2_cluster6.833.053.053.780.250.002.460.970.740.89
diseasebenefit_harmW5CCW, A vs B1 (reference)V3_random6.853.053.053.800.250.002.540.960.770.88
diseasebenefit_harmW5CCW, A vs B1 (reference)V4_within6.883.053.053.830.300.003.030.960.820.71
diseasebenefit_harmWRNaive ‘ever early PVI’V1_ignore22.1320.667.741.471.1212.921.930.760.761.00
diseasebenefit_harmWRNaive ‘ever early PVI’V2_cluster22.1320.667.741.471.1212.921.931.110.921.00
diseasebenefit_harmWRNaive ‘ever early PVI’V3_random19.8920.667.74-0.771.0612.921.911.080.961.00
diseasebenefit_harmWRNaive ‘ever early PVI’V4_within17.9820.667.74-2.681.0812.922.181.040.751.00
diseasebenefit_harmWRLandmark at 6 monthsV1_ignore22.2411.107.7411.141.053.361.930.790.001.00
diseasebenefit_harmWRLandmark at 6 monthsV2_cluster22.2411.107.7411.141.053.361.931.120.001.00
diseasebenefit_harmWRLandmark at 6 monthsV3_random19.1311.107.748.031.033.361.951.060.001.00
diseasebenefit_harmWRLandmark at 6 monthsV4_within18.4211.107.747.321.033.362.231.050.031.00
diseasebenefit_harmWRSequential trials, ITTV1_ignore20.358.937.7411.410.201.191.990.740.001.00
diseasebenefit_harmWRSequential trials, ITTV2_cluster20.358.937.7411.410.201.191.991.080.001.00
diseasebenefit_harmWRSequential trials, ITTV3_random17.098.937.748.160.201.191.991.010.001.00
diseasebenefit_harmWRSequential trials, ITTV4_within16.178.937.747.230.221.192.230.990.071.00
diseasebenefit_harmWRCCW, A vs B2V1_ignore17.147.847.749.300.430.104.270.790.241.00
diseasebenefit_harmWRCCW, A vs B2V2_cluster17.147.847.749.300.430.104.270.850.271.00
diseasebenefit_harmWRCCW, A vs B2V3_random14.967.847.747.120.460.104.560.880.641.00
diseasebenefit_harmWRCCW, A vs B2V4_within14.667.847.746.820.500.105.000.900.710.95
diseasebenefit_harmWRCCW, A vs B1 (reference)V1_ignore17.047.747.749.300.430.004.270.790.231.00
diseasebenefit_harmWRCCW, A vs B1 (reference)V2_cluster17.047.747.749.300.430.004.270.850.281.00
diseasebenefit_harmWRCCW, A vs B1 (reference)V3_random14.837.747.747.090.460.004.560.880.621.00
diseasebenefit_harmWRCCW, A vs B1 (reference)V4_within14.447.747.746.700.500.005.000.900.720.95
diseaseharmW0Naive ‘ever early PVI’V1_ignore6.897.185.11-0.290.062.071.650.850.881.00
diseaseharmW0Naive ‘ever early PVI’V2_cluster6.897.185.11-0.290.062.071.650.850.881.00
diseaseharmW0Naive ‘ever early PVI’V3_random7.027.185.11-0.160.062.071.690.850.891.00
diseaseharmW0Naive ‘ever early PVI’V4_within6.287.185.11-0.900.122.072.030.870.800.98
diseaseharmW0Landmark at 6 monthsV1_ignore7.857.955.11-0.100.162.843.610.920.920.81
diseaseharmW0Landmark at 6 monthsV2_cluster7.857.955.11-0.100.162.843.610.920.920.80
diseaseharmW0Landmark at 6 monthsV3_random7.807.955.11-0.150.152.843.620.910.910.78
diseaseharmW0Landmark at 6 monthsV4_within7.297.955.11-0.660.292.844.590.880.870.50
diseaseharmW0Sequential trials, ITTV1_ignore5.705.715.11-0.010.150.611.460.860.910.99
diseaseharmW0Sequential trials, ITTV2_cluster5.705.715.11-0.010.150.611.460.860.910.99
diseaseharmW0Sequential trials, ITTV3_random5.685.715.11-0.030.160.611.550.830.910.99
diseaseharmW0Sequential trials, ITTV4_within5.435.715.11-0.290.190.611.860.830.890.94
diseaseharmW0CCW, A vs B2V1_ignore5.325.155.110.170.280.052.811.030.960.44
diseaseharmW0CCW, A vs B2V2_cluster5.325.155.110.170.280.052.811.030.960.46
diseaseharmW0CCW, A vs B2V3_random5.385.155.110.230.290.052.911.020.970.43
diseaseharmW0CCW, A vs B2V4_within5.295.155.110.130.360.053.610.960.930.32
diseaseharmW0CCW, A vs B1 (reference)V1_ignore5.285.115.110.170.280.002.821.030.960.44
diseaseharmW0CCW, A vs B1 (reference)V2_cluster5.285.115.110.170.280.002.821.030.960.46
diseaseharmW0CCW, A vs B1 (reference)V3_random5.345.115.110.230.290.002.911.020.970.43
diseaseharmW0CCW, A vs B1 (reference)V4_within5.245.115.110.130.360.003.610.960.930.32
diseaseharmW1Naive ‘ever early PVI’V1_ignore10.339.807.180.520.102.621.511.020.951.00
diseaseharmW1Naive ‘ever early PVI’V2_cluster10.339.807.180.520.102.621.511.060.961.00
diseaseharmW1Naive ‘ever early PVI’V3_random10.199.807.180.390.082.621.541.040.971.00
diseaseharmW1Naive ‘ever early PVI’V4_within9.119.807.18-0.700.142.622.130.900.851.00
diseaseharmW1Landmark at 6 monthsV1_ignore23.7019.857.183.850.3812.6712.660.550.840.96
diseaseharmW1Landmark at 6 monthsV2_cluster23.7019.857.183.850.3812.6712.660.580.840.96
diseaseharmW1Landmark at 6 monthsV3_random22.5419.857.182.690.3212.6712.590.560.830.96
diseaseharmW1Landmark at 6 monthsV4_within21.3619.857.181.510.5312.6713.130.580.830.94
diseaseharmW1Sequential trials, ITTV1_ignore9.177.987.181.190.150.801.470.960.911.00
diseaseharmW1Sequential trials, ITTV2_cluster9.177.987.181.190.150.801.471.010.921.00
diseaseharmW1Sequential trials, ITTV3_random8.897.987.180.920.150.801.481.000.951.00
diseaseharmW1Sequential trials, ITTV4_within8.207.987.180.220.200.801.950.880.891.00
diseaseharmW1CCW, A vs B2V1_ignore8.367.267.181.110.280.072.851.070.960.85
diseaseharmW1CCW, A vs B2V2_cluster8.367.267.181.110.280.072.851.070.960.85
diseaseharmW1CCW, A vs B2V3_random8.157.267.180.890.300.072.951.040.960.81
diseaseharmW1CCW, A vs B2V4_within7.617.267.180.350.360.073.580.970.940.67
diseaseharmW1CCW, A vs B1 (reference)V1_ignore8.297.187.181.110.280.002.851.070.960.85
diseaseharmW1CCW, A vs B1 (reference)V2_cluster8.297.187.181.110.280.002.851.070.960.85
diseaseharmW1CCW, A vs B1 (reference)V3_random8.087.187.180.900.300.002.951.040.960.81
diseaseharmW1CCW, A vs B1 (reference)V4_within7.537.187.180.340.360.003.570.970.940.66
diseaseharmW1_rho0Naive ‘ever early PVI’V1_ignore9.209.507.12-0.310.082.381.411.070.951.00
diseaseharmW1_rho0Naive ‘ever early PVI’V2_cluster9.209.507.12-0.310.082.381.411.090.961.00
diseaseharmW1_rho0Naive ‘ever early PVI’V3_random9.359.507.12-0.160.072.381.451.080.971.00
diseaseharmW1_rho0Naive ‘ever early PVI’V4_within8.709.507.12-0.810.132.381.841.040.921.00
diseaseharmW1_rho0Landmark at 6 monthsV1_ignore21.2020.087.121.120.3512.9611.580.620.800.95
diseaseharmW1_rho0Landmark at 6 monthsV2_cluster21.2020.087.121.120.3512.9611.580.640.780.96
diseaseharmW1_rho0Landmark at 6 monthsV3_random20.8320.087.120.750.2812.9611.510.630.750.94
diseaseharmW1_rho0Landmark at 6 monthsV4_within20.8520.087.120.770.5212.9613.190.620.720.86
diseaseharmW1_rho0Sequential trials, ITTV1_ignore8.037.977.120.060.130.841.251.090.971.00
diseaseharmW1_rho0Sequential trials, ITTV2_cluster8.037.977.120.060.130.841.251.120.981.00
diseaseharmW1_rho0Sequential trials, ITTV3_random8.047.977.120.070.130.841.301.100.981.00
diseaseharmW1_rho0Sequential trials, ITTV4_within7.837.977.12-0.140.180.841.750.980.961.00
diseaseharmW1_rho0CCW, A vs B2V1_ignore7.427.197.120.230.300.062.961.030.920.78
diseaseharmW1_rho0CCW, A vs B2V2_cluster7.427.197.120.230.300.062.961.030.920.77
diseaseharmW1_rho0CCW, A vs B2V3_random7.557.197.120.360.310.063.101.020.900.75
diseaseharmW1_rho0CCW, A vs B2V4_within7.437.197.120.250.380.063.770.970.900.61
diseaseharmW1_rho0CCW, A vs B1 (reference)V1_ignore7.367.127.120.230.300.002.971.030.920.77
diseaseharmW1_rho0CCW, A vs B1 (reference)V2_cluster7.367.127.120.230.300.002.971.030.920.76
diseaseharmW1_rho0CCW, A vs B1 (reference)V3_random7.497.127.120.370.310.003.101.010.910.75
diseaseharmW1_rho0CCW, A vs B1 (reference)V4_within7.377.127.120.240.380.003.780.970.900.60
diseaseharmW2Naive ‘ever early PVI’V1_ignore7.2317.685.09-10.451.0012.591.510.970.001.00
diseaseharmW2Naive ‘ever early PVI’V2_cluster7.2317.685.09-10.451.0012.591.510.980.001.00
diseaseharmW2Naive ‘ever early PVI’V3_random8.7117.685.09-8.970.9412.592.640.960.130.99
diseaseharmW2Naive ‘ever early PVI’V4_within6.2217.685.09-11.461.0312.592.810.890.060.78
diseaseharmW2Landmark at 6 monthsV1_ignore8.278.205.090.070.793.114.250.920.910.76
diseaseharmW2Landmark at 6 monthsV2_cluster8.278.205.090.070.793.114.250.910.900.79
diseaseharmW2Landmark at 6 monthsV3_random7.958.205.09-0.250.703.115.010.860.880.52
diseaseharmW2Landmark at 6 monthsV4_within7.128.205.09-1.080.703.115.840.820.820.30
diseaseharmW2Sequential trials, ITTV1_ignore5.915.725.090.190.140.631.400.920.921.00
diseaseharmW2Sequential trials, ITTV2_cluster5.915.725.090.190.140.631.400.930.951.00
diseaseharmW2Sequential trials, ITTV3_random5.725.725.090.000.200.632.020.870.860.93
diseaseharmW2Sequential trials, ITTV4_within5.435.725.09-0.290.230.632.290.850.850.84
diseaseharmW2CCW, A vs B2V1_ignore5.285.155.090.130.310.063.100.980.900.36
diseaseharmW2CCW, A vs B2V2_cluster5.285.155.090.130.310.063.100.970.910.39
diseaseharmW2CCW, A vs B2V3_random5.235.155.090.080.400.063.970.960.930.20
diseaseharmW2CCW, A vs B2V4_within5.035.155.09-0.120.480.064.830.870.910.19
diseaseharmW2CCW, A vs B1 (reference)V1_ignore5.245.095.090.150.310.003.100.980.900.35
diseaseharmW2CCW, A vs B1 (reference)V2_cluster5.245.095.090.150.310.003.100.970.910.38
diseaseharmW2CCW, A vs B1 (reference)V3_random5.185.095.090.090.400.003.980.960.930.20
diseaseharmW2CCW, A vs B1 (reference)V4_within4.945.095.09-0.150.480.004.840.870.910.16
diseaseharmW3Naive ‘ever early PVI’V1_ignore8.1614.584.97-6.420.949.621.391.080.021.00
diseaseharmW3Naive ‘ever early PVI’V2_cluster8.1614.584.97-6.420.949.621.391.080.021.00
diseaseharmW3Naive ‘ever early PVI’V3_random8.4814.584.97-6.100.879.622.341.050.251.00
diseaseharmW3Naive ‘ever early PVI’V4_within5.6614.584.97-8.930.959.622.370.980.130.72
diseaseharmW3Landmark at 6 monthsV1_ignore8.436.904.971.520.671.943.391.000.960.87
diseaseharmW3Landmark at 6 monthsV2_cluster8.436.904.971.520.671.943.390.980.950.87
diseaseharmW3Landmark at 6 monthsV3_random7.176.904.970.270.541.943.810.920.940.58
diseaseharmW3Landmark at 6 monthsV4_within5.956.904.97-0.950.561.944.360.920.870.34
diseaseharmW3Sequential trials, ITTV1_ignore6.945.584.971.360.130.621.291.050.931.00
diseaseharmW3Sequential trials, ITTV2_cluster6.945.584.971.360.130.621.291.050.901.00
diseaseharmW3Sequential trials, ITTV3_random5.775.584.970.190.170.621.651.040.960.99
diseaseharmW3Sequential trials, ITTV4_within5.075.584.97-0.510.200.621.990.970.900.82
diseaseharmW3CCW, A vs B2V1_ignore5.715.024.970.700.280.052.841.070.960.49
diseaseharmW3CCW, A vs B2V2_cluster5.715.024.970.700.280.052.841.080.960.47
diseaseharmW3CCW, A vs B2V3_random5.145.024.970.120.380.053.750.970.930.19
diseaseharmW3CCW, A vs B2V4_within4.735.024.97-0.290.440.054.420.920.900.15
diseaseharmW3CCW, A vs B1 (reference)V1_ignore5.664.974.970.690.280.002.841.070.960.49
diseaseharmW3CCW, A vs B1 (reference)V2_cluster5.664.974.970.690.280.002.841.080.960.46
diseaseharmW3CCW, A vs B1 (reference)V3_random5.094.974.970.120.380.003.750.970.930.19
diseaseharmW3CCW, A vs B1 (reference)V4_within4.654.974.97-0.320.440.004.420.920.910.14
diseaseharmW4Naive ‘ever early PVI’V1_ignore6.516.734.80-0.230.071.931.371.030.931.00
diseaseharmW4Naive ‘ever early PVI’V2_cluster6.516.734.80-0.230.071.931.371.020.931.00
diseaseharmW4Naive ‘ever early PVI’V3_random6.626.734.80-0.120.071.931.460.990.951.00
diseaseharmW4Naive ‘ever early PVI’V4_within5.706.734.80-1.030.111.931.601.090.880.97
diseaseharmW4Landmark at 6 monthsV1_ignore7.187.234.80-0.050.162.433.081.040.930.70
diseaseharmW4Landmark at 6 monthsV2_cluster7.187.234.80-0.050.162.433.081.040.930.71
diseaseharmW4Landmark at 6 monthsV3_random7.257.234.800.010.142.433.341.000.950.71
diseaseharmW4Landmark at 6 monthsV4_within6.847.234.80-0.390.342.435.380.830.900.35
diseaseharmW4Sequential trials, ITTV1_ignore5.325.424.80-0.100.120.621.241.010.931.00
diseaseharmW4Sequential trials, ITTV2_cluster5.325.424.80-0.100.120.621.241.010.921.00
diseaseharmW4Sequential trials, ITTV3_random5.265.424.80-0.160.130.621.300.990.921.00
diseaseharmW4Sequential trials, ITTV4_within4.885.424.80-0.540.140.621.441.050.920.96
diseaseharmW4CCW, A vs B2V1_ignore5.115.164.80-0.050.270.362.711.040.940.40
diseaseharmW4CCW, A vs B2V2_cluster5.115.164.80-0.050.270.362.711.030.940.42
diseaseharmW4CCW, A vs B2V3_random5.225.164.800.050.280.362.801.020.950.42
diseaseharmW4CCW, A vs B2V4_within5.155.164.80-0.020.340.363.371.000.900.29
diseaseharmW4CCW, A vs B1 (reference)V1_ignore4.794.804.80-0.010.270.002.701.040.940.38
diseaseharmW4CCW, A vs B1 (reference)V2_cluster4.794.804.80-0.010.270.002.701.030.940.37
diseaseharmW4CCW, A vs B1 (reference)V3_random4.844.804.800.040.280.002.801.020.950.35
diseaseharmW4CCW, A vs B1 (reference)V4_within4.754.804.80-0.050.340.003.381.000.900.25
diseaseharmW5Naive ‘ever early PVI’V1_ignore12.747.235.115.510.112.131.091.080.001.00
diseaseharmW5Naive ‘ever early PVI’V2_cluster12.747.235.115.510.112.131.091.080.001.00
diseaseharmW5Naive ‘ever early PVI’V3_random12.837.235.115.600.112.131.101.090.001.00
diseaseharmW5Naive ‘ever early PVI’V4_within12.557.235.115.320.122.131.281.120.011.00
diseaseharmW5Landmark at 6 monthsV1_ignore10.688.165.112.520.363.061.111.080.431.00
diseaseharmW5Landmark at 6 monthsV2_cluster10.688.165.112.520.363.061.111.070.411.00
diseaseharmW5Landmark at 6 monthsV3_random10.728.165.112.550.363.061.111.100.441.00
diseaseharmW5Landmark at 6 monthsV4_within10.658.165.112.480.373.061.301.130.661.00
diseaseharmW5Sequential trials, ITTV1_ignore10.665.715.114.940.110.611.091.080.001.00
diseaseharmW5Sequential trials, ITTV2_cluster10.665.715.114.940.110.611.091.070.001.00
diseaseharmW5Sequential trials, ITTV3_random10.685.715.114.960.110.611.081.110.001.00
diseaseharmW5Sequential trials, ITTV4_within10.635.715.114.920.120.611.221.170.041.00
diseaseharmW5CCW, A vs B2V1_ignore9.585.155.114.420.250.052.491.050.600.99
diseaseharmW5CCW, A vs B2V2_cluster9.585.155.114.420.250.052.491.050.600.98
diseaseharmW5CCW, A vs B2V3_random9.555.155.114.400.260.052.581.020.610.99
diseaseharmW5CCW, A vs B2V4_within9.585.155.114.430.300.053.051.000.680.96
diseaseharmW5CCW, A vs B1 (reference)V1_ignore9.545.115.114.430.250.002.491.050.610.98
diseaseharmW5CCW, A vs B1 (reference)V2_cluster9.545.115.114.430.250.002.491.050.610.98
diseaseharmW5CCW, A vs B1 (reference)V3_random9.515.115.114.410.260.002.581.020.610.99
diseaseharmW5CCW, A vs B1 (reference)V4_within9.545.115.114.430.310.003.051.000.680.96
diseaseharmWRNaive ‘ever early PVI’V1_ignore18.7817.326.931.460.9810.391.620.880.811.00
diseaseharmWRNaive ‘ever early PVI’V2_cluster18.7817.326.931.460.9810.391.621.080.901.00
diseaseharmWRNaive ‘ever early PVI’V3_random17.8817.326.930.560.9710.392.050.940.931.00
diseaseharmWRNaive ‘ever early PVI’V4_within16.2817.326.93-1.041.0010.392.330.920.841.00
diseaseharmWRLandmark at 6 monthsV1_ignore16.7315.526.931.211.078.591.660.880.831.00
diseaseharmWRLandmark at 6 monthsV2_cluster16.7315.526.931.211.078.591.661.070.931.00
diseaseharmWRLandmark at 6 monthsV3_random15.0015.526.93-0.521.078.591.970.980.951.00
diseaseharmWRLandmark at 6 monthsV4_within14.2615.526.93-1.261.088.592.310.970.861.00
diseaseharmWRSequential trials, ITTV1_ignore16.937.686.939.250.160.751.580.910.001.00
diseaseharmWRSequential trials, ITTV2_cluster16.937.686.939.250.160.751.581.110.001.00
diseaseharmWRSequential trials, ITTV3_random15.117.686.937.430.200.752.000.930.001.00
diseaseharmWRSequential trials, ITTV4_within14.357.686.936.670.230.752.350.880.121.00
diseaseharmWRCCW, A vs B2V1_ignore14.147.086.937.060.390.153.880.840.481.00
diseaseharmWRCCW, A vs B2V2_cluster14.147.086.937.060.390.153.880.870.501.00
diseaseharmWRCCW, A vs B2V3_random13.497.086.936.410.390.153.941.010.731.00
diseaseharmWRCCW, A vs B2V4_within13.317.086.936.240.430.154.341.040.790.94
diseaseharmWRCCW, A vs B1 (reference)V1_ignore13.996.936.937.060.390.003.870.850.481.00
diseaseharmWRCCW, A vs B1 (reference)V2_cluster13.996.936.937.060.390.003.870.870.501.00
diseaseharmWRCCW, A vs B1 (reference)V3_random13.316.936.936.380.390.003.941.010.731.00
diseaseharmWRCCW, A vs B1 (reference)V4_within13.006.936.936.070.430.004.341.040.800.93
diseasenullW0Naive ‘ever early PVI’V1_ignore1.061.250.00-0.200.061.251.151.030.940.07
diseasenullW0Naive ‘ever early PVI’V2_cluster1.061.250.00-0.200.061.251.151.020.950.07
diseasenullW0Naive ‘ever early PVI’V3_random1.161.250.00-0.100.061.251.191.030.920.13
diseasenullW0Naive ‘ever early PVI’V4_within0.541.250.00-0.710.101.251.450.980.840.03
diseasenullW0Landmark at 6 monthsV1_ignore-0.16-0.300.000.140.06-0.300.991.060.970.06
diseasenullW0Landmark at 6 monthsV2_cluster-0.16-0.300.000.140.06-0.300.991.060.970.06
diseasenullW0Landmark at 6 monthsV3_random-0.12-0.300.000.180.05-0.301.021.050.970.06
diseasenullW0Landmark at 6 monthsV4_within-0.33-0.300.00-0.040.07-0.301.261.010.940.08
diseasenullW0Sequential trials, ITTV1_ignore-0.020.000.00-0.020.100.000.961.060.950.05
diseasenullW0Sequential trials, ITTV2_cluster-0.020.000.00-0.020.100.000.961.060.940.06
diseasenullW0Sequential trials, ITTV3_random-0.030.000.00-0.030.100.000.981.060.950.05
diseasenullW0Sequential trials, ITTV4_within-0.120.000.00-0.120.120.001.191.030.910.09
diseasenullW0CCW, A vs B2V1_ignore-0.330.000.00-0.330.260.002.560.860.850.15
diseasenullW0CCW, A vs B2V2_cluster-0.330.000.00-0.330.260.002.560.860.850.15
diseasenullW0CCW, A vs B2V3_random-0.210.000.00-0.210.270.002.690.860.860.14
diseasenullW0CCW, A vs B2V4_within-0.140.000.00-0.140.310.003.110.890.890.11
diseasenullW0CCW, A vs B1 (reference)V1_ignore-0.330.000.00-0.330.260.002.560.860.860.14
diseasenullW0CCW, A vs B1 (reference)V2_cluster-0.330.000.00-0.330.260.002.560.860.850.15
diseasenullW0CCW, A vs B1 (reference)V3_random-0.210.000.00-0.210.270.002.690.860.860.14
diseasenullW0CCW, A vs B1 (reference)V4_within-0.140.000.00-0.140.310.003.110.890.890.11
diseasenullW1Naive ‘ever early PVI’V1_ignore1.131.400.00-0.270.071.401.370.920.890.10
diseasenullW1Naive ‘ever early PVI’V2_cluster1.131.400.00-0.270.071.401.370.920.880.11
diseasenullW1Naive ‘ever early PVI’V3_random1.281.400.00-0.120.061.401.420.920.930.12
diseasenullW1Naive ‘ever early PVI’V4_within0.691.400.00-0.710.121.401.660.950.850.09
diseasenullW1Landmark at 6 monthsV1_ignore-0.17-0.280.000.110.05-0.280.991.100.980.05
diseasenullW1Landmark at 6 monthsV2_cluster-0.17-0.280.000.110.05-0.280.991.090.970.05
diseasenullW1Landmark at 6 monthsV3_random-0.12-0.280.000.160.05-0.281.031.060.970.04
diseasenullW1Landmark at 6 monthsV4_within-0.22-0.280.000.060.09-0.281.351.040.920.11
diseasenullW1Sequential trials, ITTV1_ignore-0.110.000.00-0.110.100.000.961.070.940.06
diseasenullW1Sequential trials, ITTV2_cluster-0.110.000.00-0.110.100.000.961.070.940.06
diseasenullW1Sequential trials, ITTV3_random-0.110.000.00-0.110.100.000.991.050.940.06
diseasenullW1Sequential trials, ITTV4_within-0.120.000.00-0.120.130.001.261.020.920.08
diseasenullW1CCW, A vs B2V1_ignore-0.010.000.00-0.010.250.002.510.950.880.12
diseasenullW1CCW, A vs B2V2_cluster-0.010.000.00-0.010.250.002.510.950.880.12
diseasenullW1CCW, A vs B2V3_random0.020.000.000.020.270.002.670.910.880.12
diseasenullW1CCW, A vs B2V4_within0.220.000.000.220.360.003.610.840.840.16
diseasenullW1CCW, A vs B1 (reference)V1_ignore-0.010.000.00-0.010.250.002.510.950.880.12
diseasenullW1CCW, A vs B1 (reference)V2_cluster-0.010.000.00-0.010.250.002.510.950.880.12
diseasenullW1CCW, A vs B1 (reference)V3_random0.020.000.000.020.270.002.670.910.880.12
diseasenullW1CCW, A vs B1 (reference)V4_within0.210.000.000.210.360.003.610.840.850.15
diseasenullW1_rho0Naive ‘ever early PVI’V1_ignore0.951.110.00-0.160.061.111.191.010.910.12
diseasenullW1_rho0Naive ‘ever early PVI’V2_cluster0.951.110.00-0.160.061.111.191.000.910.12
diseasenullW1_rho0Naive ‘ever early PVI’V3_random1.071.110.00-0.040.061.111.280.970.920.14
diseasenullW1_rho0Naive ‘ever early PVI’V4_within0.461.110.00-0.650.111.111.431.010.890.08
diseasenullW1_rho0Landmark at 6 monthsV1_ignore-0.31-0.460.000.150.05-0.460.981.060.950.11
diseasenullW1_rho0Landmark at 6 monthsV2_cluster-0.31-0.460.000.150.05-0.460.981.050.940.11
diseasenullW1_rho0Landmark at 6 monthsV3_random-0.29-0.460.000.180.05-0.461.021.030.930.10
diseasenullW1_rho0Landmark at 6 monthsV4_within-0.49-0.460.00-0.020.08-0.461.271.000.900.13
diseasenullW1_rho0Sequential trials, ITTV1_ignore-0.160.000.00-0.160.100.001.030.980.910.09
diseasenullW1_rho0Sequential trials, ITTV2_cluster-0.160.000.00-0.160.100.001.030.980.910.09
diseasenullW1_rho0Sequential trials, ITTV3_random-0.190.000.00-0.190.110.001.060.970.910.09
diseasenullW1_rho0Sequential trials, ITTV4_within-0.290.000.00-0.290.120.001.230.990.900.10
diseasenullW1_rho0CCW, A vs B2V1_ignore-0.030.000.00-0.030.250.002.480.910.900.10
diseasenullW1_rho0CCW, A vs B2V2_cluster-0.030.000.00-0.030.250.002.480.910.880.12
diseasenullW1_rho0CCW, A vs B2V3_random-0.100.000.00-0.100.240.002.450.920.900.10
diseasenullW1_rho0CCW, A vs B2V4_within-0.270.000.00-0.270.260.002.610.980.880.12
diseasenullW1_rho0CCW, A vs B1 (reference)V1_ignore-0.030.000.00-0.030.250.002.480.910.900.10
diseasenullW1_rho0CCW, A vs B1 (reference)V2_cluster-0.030.000.00-0.030.250.002.480.910.880.12
diseasenullW1_rho0CCW, A vs B1 (reference)V3_random-0.100.000.00-0.100.240.002.450.920.900.10
diseasenullW1_rho0CCW, A vs B1 (reference)V4_within-0.270.000.00-0.270.260.002.610.970.880.12
diseasenullW2Naive ‘ever early PVI’V1_ignore1.7012.650.00-10.941.1212.651.271.040.000.16
diseasenullW2Naive ‘ever early PVI’V2_cluster1.7012.650.00-10.941.1212.651.271.040.000.14
diseasenullW2Naive ‘ever early PVI’V3_random3.2712.650.00-9.371.1012.652.441.070.070.20
diseasenullW2Naive ‘ever early PVI’V4_within0.8212.650.00-11.831.1912.652.960.890.020.06
diseasenullW2Landmark at 6 monthsV1_ignore0.320.030.000.290.320.031.081.050.920.08
diseasenullW2Landmark at 6 monthsV2_cluster0.320.030.000.290.320.031.081.050.930.07
diseasenullW2Landmark at 6 monthsV3_random0.180.030.000.150.290.031.960.960.930.06
diseasenullW2Landmark at 6 monthsV4_within-0.180.030.00-0.210.330.032.860.840.910.09
diseasenullW2Sequential trials, ITTV1_ignore0.440.000.000.440.110.001.061.030.920.08
diseasenullW2Sequential trials, ITTV2_cluster0.440.000.000.440.110.001.061.030.930.07
diseasenullW2Sequential trials, ITTV3_random0.140.000.000.140.160.001.560.940.920.08
diseasenullW2Sequential trials, ITTV4_within-0.030.000.00-0.030.190.001.880.840.930.07
diseasenullW2CCW, A vs B2V1_ignore0.830.000.000.830.280.002.850.970.950.05
diseasenullW2CCW, A vs B2V2_cluster0.830.000.000.830.280.002.850.960.950.05
diseasenullW2CCW, A vs B2V3_random0.960.000.000.960.550.005.450.700.920.08
diseasenullW2CCW, A vs B2V4_within0.780.000.000.780.660.006.640.600.900.10
diseasenullW2CCW, A vs B1 (reference)V1_ignore0.840.000.000.840.280.002.840.970.950.05
diseasenullW2CCW, A vs B1 (reference)V2_cluster0.840.000.000.840.280.002.840.960.950.05
diseasenullW2CCW, A vs B1 (reference)V3_random0.960.000.000.960.540.005.450.700.920.08
diseasenullW2CCW, A vs B1 (reference)V4_within0.790.000.000.790.660.006.630.600.890.11
diseasenullW3Naive ‘ever early PVI’V1_ignore2.6310.690.00-8.070.7910.691.450.900.000.51
diseasenullW3Naive ‘ever early PVI’V2_cluster2.6310.690.00-8.070.7910.691.450.890.000.55
diseasenullW3Naive ‘ever early PVI’V3_random3.1910.690.00-7.500.7510.692.270.990.100.27
diseasenullW3Naive ‘ever early PVI’V4_within0.4710.690.00-10.220.8110.692.160.950.030.08
diseasenullW3Landmark at 6 monthsV1_ignore1.180.130.001.050.360.131.280.900.850.17
diseasenullW3Landmark at 6 monthsV2_cluster1.180.130.001.050.360.131.280.890.850.19
diseasenullW3Landmark at 6 monthsV3_random0.190.130.000.070.320.131.700.880.920.08
diseasenullW3Landmark at 6 monthsV4_within-0.550.130.00-0.680.330.132.000.880.840.15
diseasenullW3Sequential trials, ITTV1_ignore1.340.000.001.340.130.001.250.900.740.26
diseasenullW3Sequential trials, ITTV2_cluster1.340.000.001.340.130.001.250.890.750.25
diseasenullW3Sequential trials, ITTV3_random0.270.000.000.270.170.001.680.840.870.13
diseasenullW3Sequential trials, ITTV4_within-0.190.000.00-0.190.190.001.900.820.840.16
diseasenullW3CCW, A vs B2V1_ignore0.880.000.000.880.280.002.770.940.930.07
diseasenullW3CCW, A vs B2V2_cluster0.880.000.000.880.280.002.770.950.930.07
diseasenullW3CCW, A vs B2V3_random0.210.000.000.210.340.003.400.910.850.15
diseasenullW3CCW, A vs B2V4_within-0.260.000.00-0.260.370.003.680.940.810.19
diseasenullW3CCW, A vs B1 (reference)V1_ignore0.860.000.000.860.280.002.770.940.930.07
diseasenullW3CCW, A vs B1 (reference)V2_cluster0.860.000.000.860.280.002.770.950.930.07
diseasenullW3CCW, A vs B1 (reference)V3_random0.200.000.000.200.340.003.400.910.850.15
diseasenullW3CCW, A vs B1 (reference)V4_within-0.260.000.00-0.260.370.003.670.940.810.19
diseasenullW4Naive ‘ever early PVI’V1_ignore1.091.480.00-0.390.081.481.250.970.900.12
diseasenullW4Naive ‘ever early PVI’V2_cluster1.091.480.00-0.390.081.481.250.970.900.12
diseasenullW4Naive ‘ever early PVI’V3_random1.191.480.00-0.290.071.481.280.970.910.12
diseasenullW4Naive ‘ever early PVI’V4_within0.591.480.00-0.890.121.481.490.980.850.03
diseasenullW4Landmark at 6 monthsV1_ignore-0.02-0.050.000.030.06-0.051.100.990.960.05
diseasenullW4Landmark at 6 monthsV2_cluster-0.02-0.050.000.030.06-0.051.100.990.960.04
diseasenullW4Landmark at 6 monthsV3_random0.04-0.050.000.090.05-0.051.120.990.940.06
diseasenullW4Landmark at 6 monthsV4_within-0.15-0.050.00-0.100.08-0.051.351.010.920.08
diseasenullW4Sequential trials, ITTV1_ignore0.060.000.000.060.100.001.041.000.960.04
diseasenullW4Sequential trials, ITTV2_cluster0.060.000.000.060.100.001.041.000.960.04
diseasenullW4Sequential trials, ITTV3_random0.070.000.000.070.110.001.061.000.970.03
diseasenullW4Sequential trials, ITTV4_within-0.050.000.00-0.050.120.001.231.030.940.06
diseasenullW4CCW, A vs B2V1_ignore0.220.000.000.220.210.002.101.090.940.06
diseasenullW4CCW, A vs B2V2_cluster0.220.000.000.220.210.002.101.100.940.06
diseasenullW4CCW, A vs B2V3_random0.300.000.000.300.220.002.151.100.950.05
diseasenullW4CCW, A vs B2V4_within0.240.000.000.240.250.002.521.120.920.08
diseasenullW4CCW, A vs B1 (reference)V1_ignore0.250.000.000.250.210.002.111.090.950.05
diseasenullW4CCW, A vs B1 (reference)V2_cluster0.250.000.000.250.210.002.111.090.940.06
diseasenullW4CCW, A vs B1 (reference)V3_random0.280.000.000.280.220.002.161.090.950.05
diseasenullW4CCW, A vs B1 (reference)V4_within0.230.000.000.230.250.002.521.120.920.08
diseasenullW5Naive ‘ever early PVI’V1_ignore5.061.330.003.730.131.330.981.030.001.00
diseasenullW5Naive ‘ever early PVI’V2_cluster5.061.330.003.730.131.330.981.030.011.00
diseasenullW5Naive ‘ever early PVI’V3_random5.131.330.003.800.131.330.991.050.001.00
diseasenullW5Naive ‘ever early PVI’V4_within4.761.330.003.430.141.331.151.080.150.98
diseasenullW5Landmark at 6 monthsV1_ignore2.80-0.340.003.140.08-0.340.941.080.060.83
diseasenullW5Landmark at 6 monthsV2_cluster2.80-0.340.003.140.08-0.340.941.070.070.82
diseasenullW5Landmark at 6 monthsV3_random2.82-0.340.003.160.08-0.340.951.080.060.81
diseasenullW5Landmark at 6 monthsV4_within2.77-0.340.003.110.10-0.341.151.100.250.62
diseasenullW5Sequential trials, ITTV1_ignore3.430.000.003.430.090.000.921.070.050.95
diseasenullW5Sequential trials, ITTV2_cluster3.430.000.003.430.090.000.921.070.040.96
diseasenullW5Sequential trials, ITTV3_random3.430.000.003.430.090.000.921.090.050.95
diseasenullW5Sequential trials, ITTV4_within3.400.000.003.400.110.001.081.120.100.90
diseasenullW5CCW, A vs B2V1_ignore2.890.000.002.890.220.002.200.990.770.23
diseasenullW5CCW, A vs B2V2_cluster2.890.000.002.890.220.002.200.990.760.24
diseasenullW5CCW, A vs B2V3_random2.910.000.002.910.220.002.250.980.780.22
diseasenullW5CCW, A vs B2V4_within2.950.000.002.950.270.002.671.000.850.15
diseasenullW5CCW, A vs B1 (reference)V1_ignore2.890.000.002.890.220.002.200.990.770.23
diseasenullW5CCW, A vs B1 (reference)V2_cluster2.890.000.002.890.220.002.200.990.760.24
diseasenullW5CCW, A vs B1 (reference)V3_random2.910.000.002.910.220.002.250.980.780.22
diseasenullW5CCW, A vs B1 (reference)V4_within2.950.000.002.950.270.002.671.000.840.16
diseasenullWRNaive ‘ever early PVI’V1_ignore5.5910.570.00-4.980.8210.571.210.950.031.00
diseasenullWRNaive ‘ever early PVI’V2_cluster5.5910.570.00-4.980.8210.571.210.950.031.00
diseasenullWRNaive ‘ever early PVI’V3_random6.1810.570.00-4.390.8010.571.600.990.200.99
diseasenullWRNaive ‘ever early PVI’V4_within4.6110.570.00-5.960.8410.571.710.980.070.83
diseasenullWRLandmark at 6 monthsV1_ignore3.440.030.003.400.400.031.111.020.080.92
diseasenullWRLandmark at 6 monthsV2_cluster3.440.030.003.400.400.031.111.010.070.93
diseasenullWRLandmark at 6 monthsV3_random2.980.030.002.950.370.031.520.950.430.57
diseasenullWRLandmark at 6 monthsV4_within2.560.030.002.530.380.031.790.950.690.31
diseasenullWRSequential trials, ITTV1_ignore4.010.000.004.010.120.001.150.950.030.97
diseasenullWRSequential trials, ITTV2_cluster4.010.000.004.010.120.001.150.950.030.97
diseasenullWRSequential trials, ITTV3_random3.630.000.003.630.140.001.440.980.230.77
diseasenullWRSequential trials, ITTV4_within3.300.000.003.300.160.001.610.980.400.60
diseasenullWRCCW, A vs B2V1_ignore2.840.000.002.840.250.002.521.000.850.15
diseasenullWRCCW, A vs B2V2_cluster2.840.000.002.840.250.002.521.000.850.15
diseasenullWRCCW, A vs B2V3_random2.610.000.002.610.310.003.060.960.910.09
diseasenullWRCCW, A vs B2V4_within2.310.000.002.310.320.003.200.990.910.09
diseasenullWRCCW, A vs B1 (reference)V1_ignore2.800.000.002.800.250.002.531.000.850.15
diseasenullWRCCW, A vs B1 (reference)V2_cluster2.800.000.002.800.250.002.531.000.850.15
diseasenullWRCCW, A vs B1 (reference)V3_random2.550.000.002.550.310.003.070.950.910.09
diseasenullWRCCW, A vs B1 (reference)V4_within2.210.000.002.210.320.003.220.980.910.09
malebenefit_harmW0Naive ‘ever early PVI’V1_ignore20.4820.5917.36-0.110.093.231.681.130.981.00
malebenefit_harmW0Naive ‘ever early PVI’V2_cluster20.4820.5917.36-0.110.093.231.681.130.971.00
malebenefit_harmW0Naive ‘ever early PVI’V3_random20.5420.5917.36-0.040.093.231.741.110.971.00
malebenefit_harmW0Naive ‘ever early PVI’V4_within19.7020.5917.36-0.890.173.232.391.000.901.00
malebenefit_harmW0Landmark at 6 monthsV1_ignore21.0620.7617.360.290.243.405.880.910.910.99
malebenefit_harmW0Landmark at 6 monthsV2_cluster21.0620.7617.360.290.243.405.880.900.920.99
malebenefit_harmW0Landmark at 6 monthsV3_random21.0320.7617.360.270.243.405.920.900.920.99
malebenefit_harmW0Landmark at 6 monthsV4_within20.9720.7617.360.210.603.408.370.840.900.90
malebenefit_harmW0Sequential trials, ITTV1_ignore19.5219.2717.360.260.171.911.661.090.981.00
malebenefit_harmW0Sequential trials, ITTV2_cluster19.5219.2717.360.260.171.911.661.080.981.00
malebenefit_harmW0Sequential trials, ITTV3_random19.4419.2717.360.170.171.911.731.060.971.00
malebenefit_harmW0Sequential trials, ITTV4_within18.9719.2717.36-0.290.231.912.330.930.941.00
malebenefit_harmW0CCW, A vs B2V1_ignore17.7117.5517.360.160.440.194.390.930.921.00
malebenefit_harmW0CCW, A vs B2V2_cluster17.7117.5517.360.160.440.194.390.920.921.00
malebenefit_harmW0CCW, A vs B2V3_random17.7117.5517.360.150.440.194.390.930.911.00
malebenefit_harmW0CCW, A vs B2V4_within17.5717.5517.360.020.490.194.910.980.930.99
malebenefit_harmW0CCW, A vs B1 (reference)V1_ignore17.5217.3617.360.170.440.004.390.920.921.00
malebenefit_harmW0CCW, A vs B1 (reference)V2_cluster17.5217.3617.360.170.440.004.390.920.921.00
malebenefit_harmW0CCW, A vs B1 (reference)V3_random17.5217.3617.360.160.440.004.400.930.911.00
malebenefit_harmW0CCW, A vs B1 (reference)V4_within17.3717.3617.360.010.490.004.910.980.930.98
malebenefit_harmW1Naive ‘ever early PVI’V1_ignore25.0322.7619.952.270.132.812.250.880.821.00
malebenefit_harmW1Naive ‘ever early PVI’V2_cluster25.0322.7619.952.270.132.812.250.970.851.00
malebenefit_harmW1Naive ‘ever early PVI’V3_random24.4922.7619.951.730.102.812.170.990.901.00
malebenefit_harmW1Naive ‘ever early PVI’V4_within22.9222.7619.950.160.142.812.551.020.961.00
malebenefit_harmW1Landmark at 6 monthsV1_ignore25.5721.6119.953.960.281.676.350.880.850.98
malebenefit_harmW1Landmark at 6 monthsV2_cluster25.5721.6119.953.960.281.676.350.900.860.98
malebenefit_harmW1Landmark at 6 monthsV3_random25.3621.6119.953.740.261.676.300.900.860.98
malebenefit_harmW1Landmark at 6 monthsV4_within23.7021.6119.952.090.591.678.400.850.900.91
malebenefit_harmW1Sequential trials, ITTV1_ignore24.2021.7519.952.450.221.802.200.860.741.00
malebenefit_harmW1Sequential trials, ITTV2_cluster24.2021.7519.952.450.221.802.200.950.801.00
malebenefit_harmW1Sequential trials, ITTV3_random23.5121.7519.951.760.211.802.130.970.881.00
malebenefit_harmW1Sequential trials, ITTV4_within22.3021.7519.950.540.241.802.440.990.941.00
malebenefit_harmW1CCW, A vs B2V1_ignore22.3620.2119.952.150.470.264.700.890.851.00
malebenefit_harmW1CCW, A vs B2V2_cluster22.3620.2119.952.150.470.264.700.910.871.00
malebenefit_harmW1CCW, A vs B2V3_random21.9120.2119.951.700.470.264.740.910.881.00
malebenefit_harmW1CCW, A vs B2V4_within20.9820.2119.950.770.560.265.590.890.871.00
malebenefit_harmW1CCW, A vs B1 (reference)V1_ignore22.1019.9519.952.150.470.004.700.890.851.00
malebenefit_harmW1CCW, A vs B1 (reference)V2_cluster22.1019.9519.952.150.470.004.700.910.871.00
malebenefit_harmW1CCW, A vs B1 (reference)V3_random21.6519.9519.951.700.470.004.750.910.881.00
malebenefit_harmW1CCW, A vs B1 (reference)V4_within20.6919.9519.950.740.560.005.590.890.870.99
malebenefit_harmW1_rho0Naive ‘ever early PVI’V1_ignore23.8124.0220.65-0.210.123.371.861.050.931.00
malebenefit_harmW1_rho0Naive ‘ever early PVI’V2_cluster23.8124.0220.65-0.210.123.371.861.150.981.00
malebenefit_harmW1_rho0Naive ‘ever early PVI’V3_random23.9524.0220.65-0.080.093.371.921.120.961.00
malebenefit_harmW1_rho0Naive ‘ever early PVI’V4_within23.3424.0220.65-0.680.143.372.581.030.941.00
malebenefit_harmW1_rho0Landmark at 6 monthsV1_ignore25.3023.9120.651.400.293.256.000.920.931.00
malebenefit_harmW1_rho0Landmark at 6 monthsV2_cluster25.3023.9120.651.400.293.256.000.930.941.00
malebenefit_harmW1_rho0Landmark at 6 monthsV3_random25.1923.9120.651.280.283.256.000.930.921.00
malebenefit_harmW1_rho0Landmark at 6 monthsV4_within23.4723.9120.65-0.440.643.258.290.910.910.89
malebenefit_harmW1_rho0Sequential trials, ITTV1_ignore22.9322.5520.650.390.181.891.841.020.961.00
malebenefit_harmW1_rho0Sequential trials, ITTV2_cluster22.9322.5520.650.390.181.891.841.120.981.00
malebenefit_harmW1_rho0Sequential trials, ITTV3_random22.9322.5520.650.390.191.891.901.090.981.00
malebenefit_harmW1_rho0Sequential trials, ITTV4_within22.6122.5520.650.060.251.892.470.980.941.00
malebenefit_harmW1_rho0CCW, A vs B2V1_ignore21.3320.8920.650.440.430.244.280.980.981.00
malebenefit_harmW1_rho0CCW, A vs B2V2_cluster21.3320.8920.650.440.430.244.281.000.981.00
malebenefit_harmW1_rho0CCW, A vs B2V3_random21.4420.8920.650.540.450.244.460.980.941.00
malebenefit_harmW1_rho0CCW, A vs B2V4_within21.4320.8920.650.540.540.245.430.960.940.99
malebenefit_harmW1_rho0CCW, A vs B1 (reference)V1_ignore21.0920.6520.650.440.430.004.280.980.981.00
malebenefit_harmW1_rho0CCW, A vs B1 (reference)V2_cluster21.0920.6520.650.440.430.004.281.000.981.00
malebenefit_harmW1_rho0CCW, A vs B1 (reference)V3_random21.2020.6520.650.540.450.004.460.980.941.00
malebenefit_harmW1_rho0CCW, A vs B1 (reference)V4_within21.1720.6520.650.520.540.005.430.960.940.99
malebenefit_harmW2Naive ‘ever early PVI’V1_ignore21.5927.0417.96-5.460.839.091.980.970.231.00
malebenefit_harmW2Naive ‘ever early PVI’V2_cluster21.5927.0417.96-5.460.839.091.980.970.221.00
malebenefit_harmW2Naive ‘ever early PVI’V3_random22.1227.0417.96-4.920.849.092.990.990.531.00
malebenefit_harmW2Naive ‘ever early PVI’V4_within19.7627.0417.96-7.280.899.093.560.990.421.00
malebenefit_harmW2Landmark at 6 monthsV1_ignore21.6118.8317.962.771.070.875.121.050.940.98
malebenefit_harmW2Landmark at 6 monthsV2_cluster21.6118.8317.962.771.070.875.121.040.920.99
malebenefit_harmW2Landmark at 6 monthsV3_random22.0918.8317.963.261.010.875.511.050.920.97
malebenefit_harmW2Landmark at 6 monthsV4_within21.1718.8317.962.341.150.878.190.920.920.89
malebenefit_harmW2Sequential trials, ITTV1_ignore20.5319.9217.960.620.191.961.930.950.941.00
malebenefit_harmW2Sequential trials, ITTV2_cluster20.5319.9217.960.620.191.961.930.940.941.00
malebenefit_harmW2Sequential trials, ITTV3_random19.8819.9217.96-0.040.271.962.660.950.921.00
malebenefit_harmW2Sequential trials, ITTV4_within19.0719.9217.96-0.840.301.962.970.940.911.00
malebenefit_harmW2CCW, A vs B2V1_ignore18.5918.2217.960.360.410.274.111.030.961.00
malebenefit_harmW2CCW, A vs B2V2_cluster18.5918.2217.960.360.410.274.111.010.961.00
malebenefit_harmW2CCW, A vs B2V3_random18.7318.2217.960.500.590.275.910.950.900.96
malebenefit_harmW2CCW, A vs B2V4_within18.8818.2217.960.650.750.277.460.860.900.91
malebenefit_harmW2CCW, A vs B1 (reference)V1_ignore18.3717.9617.960.410.410.004.111.030.971.00
malebenefit_harmW2CCW, A vs B1 (reference)V2_cluster18.3717.9617.960.410.410.004.111.020.961.00
malebenefit_harmW2CCW, A vs B1 (reference)V3_random18.4717.9617.960.520.590.005.900.950.900.96
malebenefit_harmW2CCW, A vs B1 (reference)V4_within18.4117.9617.960.450.750.007.460.860.910.89
malebenefit_harmW3Naive ‘ever early PVI’V1_ignore23.3421.8511.261.480.8610.592.350.830.801.00
malebenefit_harmW3Naive ‘ever early PVI’V2_cluster23.3421.8511.261.480.8610.592.350.900.831.00
malebenefit_harmW3Naive ‘ever early PVI’V3_random18.8021.8511.26-3.050.7910.593.020.960.761.00
malebenefit_harmW3Naive ‘ever early PVI’V4_within15.6021.8511.26-6.250.8410.593.440.960.440.99
malebenefit_harmW3Landmark at 6 monthsV1_ignore21.7614.6411.267.120.843.385.550.970.800.98
malebenefit_harmW3Landmark at 6 monthsV2_cluster21.7614.6411.267.120.843.385.550.970.780.98
malebenefit_harmW3Landmark at 6 monthsV3_random19.7314.6411.265.090.723.385.990.920.840.97
malebenefit_harmW3Landmark at 6 monthsV4_within17.6814.6411.263.030.843.389.040.760.850.74
malebenefit_harmW3Sequential trials, ITTV1_ignore22.1712.6311.269.540.221.372.210.840.001.00
malebenefit_harmW3Sequential trials, ITTV2_cluster22.1712.6311.269.540.221.372.210.920.001.00
malebenefit_harmW3Sequential trials, ITTV3_random16.2512.6311.263.620.251.372.490.960.681.00
malebenefit_harmW3Sequential trials, ITTV4_within14.8212.6311.262.190.281.372.780.940.881.00
malebenefit_harmW3CCW, A vs B2V1_ignore18.6311.5711.267.070.450.304.450.960.651.00
malebenefit_harmW3CCW, A vs B2V2_cluster18.6311.5711.267.070.450.304.450.960.641.00
malebenefit_harmW3CCW, A vs B2V3_random14.8111.5711.263.240.580.305.790.920.860.84
malebenefit_harmW3CCW, A vs B2V4_within14.0211.5711.262.460.670.306.670.870.870.70
malebenefit_harmW3CCW, A vs B1 (reference)V1_ignore18.4011.2611.267.140.450.004.450.960.641.00
malebenefit_harmW3CCW, A vs B1 (reference)V2_cluster18.4011.2611.267.140.450.004.450.960.641.00
malebenefit_harmW3CCW, A vs B1 (reference)V3_random14.5211.2611.263.260.580.005.800.920.860.81
malebenefit_harmW3CCW, A vs B1 (reference)V4_within13.4811.2611.262.220.670.006.680.870.880.66
malebenefit_harmW4Naive ‘ever early PVI’V1_ignore19.2319.5116.42-0.290.103.101.761.080.951.00
malebenefit_harmW4Naive ‘ever early PVI’V2_cluster19.2319.5116.42-0.290.103.101.761.080.941.00
malebenefit_harmW4Naive ‘ever early PVI’V3_random19.3719.5116.42-0.140.093.101.811.080.961.00
malebenefit_harmW4Naive ‘ever early PVI’V4_within18.7219.5116.42-0.800.153.102.251.080.951.00
malebenefit_harmW4Landmark at 6 monthsV1_ignore19.2419.1516.420.090.222.735.570.940.950.99
malebenefit_harmW4Landmark at 6 monthsV2_cluster19.2419.1516.420.090.222.735.570.940.940.99
malebenefit_harmW4Landmark at 6 monthsV3_random19.1819.1516.420.030.212.735.530.950.930.99
malebenefit_harmW4Landmark at 6 monthsV4_within17.5919.1516.42-1.560.512.738.200.840.870.78
malebenefit_harmW4Sequential trials, ITTV1_ignore18.2818.3416.42-0.060.171.921.731.050.951.00
malebenefit_harmW4Sequential trials, ITTV2_cluster18.2818.3416.42-0.060.171.921.731.050.961.00
malebenefit_harmW4Sequential trials, ITTV3_random18.2918.3416.42-0.050.181.921.781.050.971.00
malebenefit_harmW4Sequential trials, ITTV4_within18.0418.3416.42-0.300.221.922.161.020.941.00
malebenefit_harmW4CCW, A vs B2V1_ignore16.9017.5016.42-0.600.391.083.881.030.921.00
malebenefit_harmW4CCW, A vs B2V2_cluster16.9017.5016.42-0.600.391.083.881.020.931.00
malebenefit_harmW4CCW, A vs B2V3_random16.9517.5016.42-0.550.391.083.911.030.931.00
malebenefit_harmW4CCW, A vs B2V4_within16.5217.5016.42-0.980.471.084.661.000.910.97
malebenefit_harmW4CCW, A vs B1 (reference)V1_ignore15.8616.4216.42-0.550.390.003.861.030.931.00
malebenefit_harmW4CCW, A vs B1 (reference)V2_cluster15.8616.4216.42-0.550.390.003.861.030.931.00
malebenefit_harmW4CCW, A vs B1 (reference)V3_random15.8616.4216.42-0.560.390.003.901.040.931.00
malebenefit_harmW4CCW, A vs B1 (reference)V4_within15.3516.4216.42-1.070.470.004.661.000.910.95
malebenefit_harmW5Naive ‘ever early PVI’V1_ignore26.9020.5517.366.350.203.201.420.980.001.00
malebenefit_harmW5Naive ‘ever early PVI’V2_cluster26.9020.5517.366.350.203.201.420.980.001.00
malebenefit_harmW5Naive ‘ever early PVI’V3_random26.9120.5517.366.360.193.201.460.970.001.00
malebenefit_harmW5Naive ‘ever early PVI’V4_within26.6120.5517.366.060.203.201.770.960.051.00
malebenefit_harmW5Landmark at 6 monthsV1_ignore27.2121.1317.366.080.583.771.411.010.001.00
malebenefit_harmW5Landmark at 6 monthsV2_cluster27.2121.1317.366.080.583.771.411.010.001.00
malebenefit_harmW5Landmark at 6 monthsV3_random27.1721.1317.366.040.583.771.441.010.001.00
malebenefit_harmW5Landmark at 6 monthsV4_within27.0721.1317.365.940.613.771.800.970.091.00
malebenefit_harmW5Sequential trials, ITTV1_ignore25.0119.2717.365.750.151.911.520.950.021.00
malebenefit_harmW5Sequential trials, ITTV2_cluster25.0119.2717.365.750.151.911.520.950.021.00
malebenefit_harmW5Sequential trials, ITTV3_random24.9619.2717.365.700.161.911.570.940.031.00
malebenefit_harmW5Sequential trials, ITTV4_within24.7219.2717.365.450.191.911.930.910.131.00
malebenefit_harmW5CCW, A vs B2V1_ignore22.7217.5517.365.170.330.193.340.970.621.00
malebenefit_harmW5CCW, A vs B2V2_cluster22.7217.5517.365.170.330.193.340.970.631.00
malebenefit_harmW5CCW, A vs B2V3_random22.8717.5517.365.310.340.193.360.980.581.00
malebenefit_harmW5CCW, A vs B2V4_within23.1517.5517.365.600.410.194.120.950.671.00
malebenefit_harmW5CCW, A vs B1 (reference)V1_ignore22.5417.3617.365.180.330.003.340.970.621.00
malebenefit_harmW5CCW, A vs B1 (reference)V2_cluster22.5417.3617.365.180.330.003.340.970.631.00
malebenefit_harmW5CCW, A vs B1 (reference)V3_random22.6817.3617.365.330.340.003.360.980.581.00
malebenefit_harmW5CCW, A vs B1 (reference)V4_within22.9517.3617.365.590.410.004.120.950.671.00
malebenefit_harmWRNaive ‘ever early PVI’V1_ignore34.0925.3213.738.781.0511.592.190.730.001.00
malebenefit_harmWRNaive ‘ever early PVI’V2_cluster34.0925.3213.738.781.0511.592.191.050.011.00
malebenefit_harmWRNaive ‘ever early PVI’V3_random28.6525.3213.733.331.0011.592.051.090.731.00
malebenefit_harmWRNaive ‘ever early PVI’V4_within26.5525.3213.731.231.0211.592.261.110.941.00
malebenefit_harmWRLandmark at 6 monthsV1_ignore34.4217.8413.7316.581.104.112.220.740.001.00
malebenefit_harmWRLandmark at 6 monthsV2_cluster34.4217.8413.7316.581.104.112.221.050.001.00
malebenefit_harmWRLandmark at 6 monthsV3_random28.3117.8413.7310.481.094.112.101.090.001.00
malebenefit_harmWRLandmark at 6 monthsV4_within27.2017.8413.739.361.104.112.341.110.011.00
malebenefit_harmWRSequential trials, ITTV1_ignore32.4815.1213.7317.360.231.392.340.700.001.00
malebenefit_harmWRSequential trials, ITTV2_cluster32.4815.1213.7317.360.231.392.341.000.001.00
malebenefit_harmWRSequential trials, ITTV3_random26.0115.1213.7310.890.221.392.241.010.001.00
malebenefit_harmWRSequential trials, ITTV4_within24.5915.1213.739.480.241.392.441.020.011.00
malebenefit_harmWRCCW, A vs B2V1_ignore28.9114.1113.7314.790.450.384.550.850.051.00
malebenefit_harmWRCCW, A vs B2V2_cluster28.9114.1113.7314.790.450.384.550.910.071.00
malebenefit_harmWRCCW, A vs B2V3_random24.2714.1113.7310.150.480.384.790.980.431.00
malebenefit_harmWRCCW, A vs B2V4_within23.9614.1113.739.850.540.385.420.970.531.00
malebenefit_harmWRCCW, A vs B1 (reference)V1_ignore28.6013.7313.7314.870.450.004.530.850.041.00
malebenefit_harmWRCCW, A vs B1 (reference)V2_cluster28.6013.7313.7314.870.450.004.530.910.071.00
malebenefit_harmWRCCW, A vs B1 (reference)V3_random23.8713.7313.7310.140.480.004.770.980.421.00
malebenefit_harmWRCCW, A vs B1 (reference)V4_within23.2013.7313.739.470.540.005.380.970.561.00
maleharmW0Naive ‘ever early PVI’V1_ignore23.1323.3320.68-0.200.092.661.970.990.951.00
maleharmW0Naive ‘ever early PVI’V2_cluster23.1323.3320.68-0.200.092.661.970.980.941.00
maleharmW0Naive ‘ever early PVI’V3_random23.2723.3320.68-0.070.082.662.040.970.941.00
maleharmW0Naive ‘ever early PVI’V4_within22.5823.3320.68-0.750.152.662.441.010.931.00
maleharmW0Landmark at 6 monthsV1_ignore24.7324.6720.680.060.214.004.710.980.971.00
maleharmW0Landmark at 6 monthsV2_cluster24.7324.6720.680.060.214.004.710.980.971.00
maleharmW0Landmark at 6 monthsV3_random24.6724.6720.680.000.194.004.840.960.961.00
maleharmW0Landmark at 6 monthsV4_within24.1724.6720.68-0.500.384.006.490.890.920.96
maleharmW0Sequential trials, ITTV1_ignore22.1522.2320.68-0.070.191.551.890.980.931.00
maleharmW0Sequential trials, ITTV2_cluster22.1522.2320.68-0.070.191.551.890.980.941.00
maleharmW0Sequential trials, ITTV3_random22.1622.2320.68-0.070.201.552.010.950.941.00
maleharmW0Sequential trials, ITTV4_within21.8122.2320.68-0.410.241.552.400.950.931.00
maleharmW0CCW, A vs B2V1_ignore21.2220.9820.680.240.430.304.350.970.951.00
maleharmW0CCW, A vs B2V2_cluster21.2220.9820.680.240.430.304.350.970.921.00
maleharmW0CCW, A vs B2V3_random21.2320.9820.680.250.450.304.540.950.961.00
maleharmW0CCW, A vs B2V4_within21.4020.9820.680.420.550.305.510.940.911.00
maleharmW0CCW, A vs B1 (reference)V1_ignore20.9320.6820.680.250.440.004.350.970.941.00
maleharmW0CCW, A vs B1 (reference)V2_cluster20.9320.6820.680.250.440.004.350.970.921.00
maleharmW0CCW, A vs B1 (reference)V3_random20.9420.6820.680.260.450.004.540.950.961.00
maleharmW0CCW, A vs B1 (reference)V4_within21.0820.6820.680.410.550.005.520.940.911.00
maleharmW1Naive ‘ever early PVI’V1_ignore25.9524.7321.551.220.123.181.801.120.951.00
maleharmW1Naive ‘ever early PVI’V2_cluster25.9524.7321.551.220.123.181.801.170.951.00
maleharmW1Naive ‘ever early PVI’V3_random25.6224.7321.550.890.103.181.871.130.971.00
maleharmW1Naive ‘ever early PVI’V4_within24.3924.7321.55-0.340.183.182.621.010.951.00
maleharmW1Landmark at 6 monthsV1_ignore41.6137.4121.554.190.3515.8710.290.610.880.99
maleharmW1Landmark at 6 monthsV2_cluster41.6137.4121.554.190.3515.8710.290.630.880.99
maleharmW1Landmark at 6 monthsV3_random40.3737.4121.552.960.2915.8710.370.610.860.99
maleharmW1Landmark at 6 monthsV4_within38.7137.4121.551.300.4915.8711.360.620.831.00
maleharmW1Sequential trials, ITTV1_ignore25.0323.0421.551.990.171.491.751.090.891.00
maleharmW1Sequential trials, ITTV2_cluster25.0323.0421.551.990.171.491.751.150.921.00
maleharmW1Sequential trials, ITTV3_random24.5523.0421.551.510.181.491.801.120.941.00
maleharmW1Sequential trials, ITTV4_within23.5523.0421.550.510.241.492.351.010.951.00
maleharmW1CCW, A vs B2V1_ignore23.5221.8921.551.630.440.344.350.970.921.00
maleharmW1CCW, A vs B2V2_cluster23.5221.8921.551.630.440.344.350.980.911.00
maleharmW1CCW, A vs B2V3_random23.1021.8921.551.210.440.344.410.970.921.00
maleharmW1CCW, A vs B2V4_within22.4121.8921.550.520.520.345.160.960.910.99
maleharmW1CCW, A vs B1 (reference)V1_ignore23.1921.5521.551.650.440.004.350.980.921.00
maleharmW1CCW, A vs B1 (reference)V2_cluster23.1921.5521.551.650.440.004.350.980.911.00
maleharmW1CCW, A vs B1 (reference)V3_random22.7721.5521.551.220.440.004.410.970.921.00
maleharmW1CCW, A vs B1 (reference)V4_within22.0421.5521.550.490.520.005.160.960.910.99
maleharmW1_rho0Naive ‘ever early PVI’V1_ignore24.8325.0622.33-0.230.102.741.741.140.991.00
maleharmW1_rho0Naive ‘ever early PVI’V2_cluster24.8325.0622.33-0.230.102.741.741.180.991.00
maleharmW1_rho0Naive ‘ever early PVI’V3_random24.9625.0622.33-0.100.092.741.791.160.991.00
maleharmW1_rho0Naive ‘ever early PVI’V4_within24.1325.0622.33-0.940.172.742.301.120.971.00
maleharmW1_rho0Landmark at 6 monthsV1_ignore39.1937.8022.331.390.3115.4710.020.650.830.99
maleharmW1_rho0Landmark at 6 monthsV2_cluster39.1937.8022.331.390.3115.4710.020.670.840.99
maleharmW1_rho0Landmark at 6 monthsV3_random38.7537.8022.330.950.2615.4710.060.660.830.99
maleharmW1_rho0Landmark at 6 monthsV4_within38.0237.8022.330.220.5415.4711.710.650.801.00
maleharmW1_rho0Sequential trials, ITTV1_ignore23.9023.9222.33-0.020.171.591.691.130.991.00
maleharmW1_rho0Sequential trials, ITTV2_cluster23.9023.9222.33-0.020.171.591.691.180.991.00
maleharmW1_rho0Sequential trials, ITTV3_random23.8823.9222.33-0.030.171.591.731.160.981.00
maleharmW1_rho0Sequential trials, ITTV4_within23.3223.9222.33-0.590.221.592.161.100.991.00
maleharmW1_rho0CCW, A vs B2V1_ignore22.9022.6522.330.250.450.324.460.950.921.00
maleharmW1_rho0CCW, A vs B2V2_cluster22.9022.6522.330.250.450.324.460.960.931.00
maleharmW1_rho0CCW, A vs B2V3_random22.9122.6522.330.260.450.324.540.960.921.00
maleharmW1_rho0CCW, A vs B2V4_within22.2422.6522.33-0.410.530.325.330.930.911.00
maleharmW1_rho0CCW, A vs B1 (reference)V1_ignore22.5822.3322.330.250.450.004.460.950.921.00
maleharmW1_rho0CCW, A vs B1 (reference)V2_cluster22.5822.3322.330.250.450.004.460.960.921.00
maleharmW1_rho0CCW, A vs B1 (reference)V3_random22.5922.3322.330.260.450.004.540.960.921.00
maleharmW1_rho0CCW, A vs B1 (reference)V4_within21.8922.3322.33-0.440.530.005.330.930.911.00
maleharmW2Naive ‘ever early PVI’V1_ignore24.5232.0021.28-7.470.8410.712.140.920.071.00
maleharmW2Naive ‘ever early PVI’V2_cluster24.5232.0021.28-7.470.8410.712.140.940.071.00
maleharmW2Naive ‘ever early PVI’V3_random25.4432.0021.28-6.560.7910.712.891.060.321.00
maleharmW2Naive ‘ever early PVI’V4_within22.9332.0021.28-9.070.8610.713.580.990.241.00
maleharmW2Landmark at 6 monthsV1_ignore25.5926.0921.28-0.510.904.816.010.790.900.99
maleharmW2Landmark at 6 monthsV2_cluster25.5926.0921.28-0.510.904.816.010.790.880.99
maleharmW2Landmark at 6 monthsV3_random25.1126.0921.28-0.980.794.816.760.840.870.98
maleharmW2Landmark at 6 monthsV4_within23.8026.0921.28-2.290.844.818.400.800.810.93
maleharmW2Sequential trials, ITTV1_ignore23.4522.9321.280.530.201.642.020.930.931.00
maleharmW2Sequential trials, ITTV2_cluster23.4522.9321.280.530.201.642.020.940.921.00
maleharmW2Sequential trials, ITTV3_random23.2122.9321.280.280.251.642.551.010.981.00
maleharmW2Sequential trials, ITTV4_within22.3322.9321.28-0.590.291.642.890.990.941.00
maleharmW2CCW, A vs B2V1_ignore21.9221.6821.280.240.480.394.840.910.921.00
maleharmW2CCW, A vs B2V2_cluster21.9221.6821.280.240.480.394.840.890.901.00
maleharmW2CCW, A vs B2V3_random22.1321.6821.280.460.660.396.650.860.880.97
maleharmW2CCW, A vs B2V4_within21.6921.6821.280.020.750.397.530.830.920.91
maleharmW2CCW, A vs B1 (reference)V1_ignore21.6021.2821.280.320.480.004.840.910.911.00
maleharmW2CCW, A vs B1 (reference)V2_cluster21.6021.2821.280.320.480.004.840.890.921.00
maleharmW2CCW, A vs B1 (reference)V3_random21.7621.2821.280.480.670.006.650.860.870.97
maleharmW2CCW, A vs B1 (reference)V4_within21.0421.2821.28-0.250.750.007.540.830.920.90
maleharmW3Naive ‘ever early PVI’V1_ignore25.3922.5214.232.860.858.302.240.900.671.00
maleharmW3Naive ‘ever early PVI’V2_cluster25.3922.5214.232.860.858.302.240.940.701.00
maleharmW3Naive ‘ever early PVI’V3_random20.5722.5214.23-1.960.778.302.930.990.871.00
maleharmW3Naive ‘ever early PVI’V4_within17.1922.5214.23-5.340.848.303.220.990.581.00
maleharmW3Landmark at 6 monthsV1_ignore26.3916.5114.239.880.722.284.381.060.401.00
maleharmW3Landmark at 6 monthsV2_cluster26.3916.5114.239.880.722.284.381.060.381.00
maleharmW3Landmark at 6 monthsV3_random20.3916.5114.233.880.592.285.750.880.830.98
maleharmW3Landmark at 6 monthsV4_within18.2616.5114.231.750.642.286.870.850.900.88
maleharmW3Sequential trials, ITTV1_ignore24.4015.2814.239.120.221.052.190.880.001.00
maleharmW3Sequential trials, ITTV2_cluster24.4015.2814.239.120.221.052.190.930.011.00
maleharmW3Sequential trials, ITTV3_random18.2515.2814.232.970.261.052.560.940.721.00
maleharmW3Sequential trials, ITTV4_within16.6515.2814.231.370.291.052.920.900.911.00
maleharmW3CCW, A vs B2V1_ignore22.6714.6514.238.020.450.434.540.990.571.00
maleharmW3CCW, A vs B2V2_cluster22.6714.6514.238.020.450.434.541.000.591.00
maleharmW3CCW, A vs B2V3_random17.1214.6514.232.470.530.435.261.000.920.93
maleharmW3CCW, A vs B2V4_within16.2714.6514.231.620.600.435.980.960.920.86
maleharmW3CCW, A vs B1 (reference)V1_ignore22.3414.2314.238.120.450.004.530.990.571.00
maleharmW3CCW, A vs B1 (reference)V2_cluster22.3414.2314.238.120.450.004.531.000.591.00
maleharmW3CCW, A vs B1 (reference)V3_random16.7214.2314.232.500.530.005.271.000.920.93
maleharmW3CCW, A vs B1 (reference)V4_within15.5114.2314.231.290.600.006.000.960.900.83
maleharmW4Naive ‘ever early PVI’V1_ignore21.3721.4618.95-0.090.092.511.921.010.941.00
maleharmW4Naive ‘ever early PVI’V2_cluster21.3721.4618.95-0.090.092.511.921.000.931.00
maleharmW4Naive ‘ever early PVI’V3_random21.4621.4618.95-0.000.082.512.050.970.921.00
maleharmW4Naive ‘ever early PVI’V4_within20.3721.4618.95-1.090.162.512.730.910.921.00
maleharmW4Landmark at 6 monthsV1_ignore22.7322.7018.950.030.263.754.691.010.920.98
maleharmW4Landmark at 6 monthsV2_cluster22.7322.7018.950.030.263.754.691.010.920.98
maleharmW4Landmark at 6 monthsV3_random22.8322.7018.950.130.223.754.920.980.920.99
maleharmW4Landmark at 6 monthsV4_within22.4922.7018.95-0.220.413.756.650.930.910.98
maleharmW4Sequential trials, ITTV1_ignore20.4320.5318.95-0.100.191.581.870.990.961.00
maleharmW4Sequential trials, ITTV2_cluster20.4320.5318.95-0.100.191.581.870.980.961.00
maleharmW4Sequential trials, ITTV3_random20.3620.5318.95-0.170.201.582.000.940.961.00
maleharmW4Sequential trials, ITTV4_within19.7220.5318.95-0.820.261.582.600.860.891.00
maleharmW4CCW, A vs B2V1_ignore20.8920.9318.95-0.050.421.984.251.010.931.00
maleharmW4CCW, A vs B2V2_cluster20.8920.9318.95-0.050.421.984.251.000.931.00
maleharmW4CCW, A vs B2V3_random20.9620.9318.950.030.421.984.251.020.941.00
maleharmW4CCW, A vs B2V4_within20.8420.9318.95-0.090.501.984.991.000.951.00
maleharmW4CCW, A vs B1 (reference)V1_ignore19.0018.9518.950.050.430.004.271.000.940.99
maleharmW4CCW, A vs B1 (reference)V2_cluster19.0018.9518.950.050.430.004.271.000.930.99
maleharmW4CCW, A vs B1 (reference)V3_random18.9918.9518.950.040.430.004.261.020.941.00
maleharmW4CCW, A vs B1 (reference)V4_within18.7118.9518.95-0.240.500.004.991.000.940.99
maleharmW5Naive ‘ever early PVI’V1_ignore30.4323.6120.686.830.162.931.401.010.001.00
maleharmW5Naive ‘ever early PVI’V2_cluster30.4323.6120.686.830.162.931.401.010.001.00
maleharmW5Naive ‘ever early PVI’V3_random30.5223.6120.686.920.152.931.391.040.001.00
maleharmW5Naive ‘ever early PVI’V4_within30.3523.6120.686.740.162.931.571.100.021.00
maleharmW5Landmark at 6 monthsV1_ignore28.3824.5120.683.880.463.831.521.000.251.00
maleharmW5Landmark at 6 monthsV2_cluster28.3824.5120.683.880.463.831.521.000.271.00
maleharmW5Landmark at 6 monthsV3_random28.4424.5120.683.930.463.831.511.030.231.00
maleharmW5Landmark at 6 monthsV4_within28.3824.5120.683.870.463.831.741.070.461.00
maleharmW5Sequential trials, ITTV1_ignore28.4022.2320.686.180.151.551.491.000.001.00
maleharmW5Sequential trials, ITTV2_cluster28.4022.2320.686.180.151.551.491.000.001.00
maleharmW5Sequential trials, ITTV3_random28.4222.2320.686.190.151.551.491.020.001.00
maleharmW5Sequential trials, ITTV4_within28.3022.2320.686.070.171.551.701.050.091.00
maleharmW5CCW, A vs B2V1_ignore27.1120.9820.686.140.300.303.001.120.551.00
maleharmW5CCW, A vs B2V2_cluster27.1120.9820.686.140.300.303.001.130.551.00
maleharmW5CCW, A vs B2V3_random27.2620.9820.686.280.300.303.001.140.561.00
maleharmW5CCW, A vs B2V4_within27.5420.9820.686.570.380.303.821.050.581.00
maleharmW5CCW, A vs B1 (reference)V1_ignore26.8320.6820.686.150.300.003.001.120.561.00
maleharmW5CCW, A vs B1 (reference)V2_cluster26.8320.6820.686.150.300.003.001.130.541.00
maleharmW5CCW, A vs B1 (reference)V3_random26.9820.6820.686.300.300.003.011.140.561.00
maleharmW5CCW, A vs B1 (reference)V4_within27.2320.6820.686.560.380.003.821.050.581.00
maleharmWRNaive ‘ever early PVI’V1_ignore33.9323.6014.4810.320.899.122.190.730.001.00
maleharmWRNaive ‘ever early PVI’V2_cluster33.9323.6014.4810.320.899.122.190.980.001.00
maleharmWRNaive ‘ever early PVI’V3_random28.9623.6014.485.350.879.122.340.930.341.00
maleharmWRNaive ‘ever early PVI’V4_within26.9923.6014.483.390.899.122.540.960.721.00
maleharmWRLandmark at 6 monthsV1_ignore32.1225.2514.486.871.1310.762.340.730.071.00
maleharmWRLandmark at 6 monthsV2_cluster32.1225.2514.486.871.1310.762.340.960.111.00
maleharmWRLandmark at 6 monthsV3_random26.3425.2514.481.091.1510.762.350.970.931.00
maleharmWRLandmark at 6 monthsV4_within24.9725.2514.48-0.281.1710.762.650.990.961.00
maleharmWRSequential trials, ITTV1_ignore32.2815.5114.4816.770.221.032.230.740.001.00
maleharmWRSequential trials, ITTV2_cluster32.2815.5114.4816.770.221.032.230.990.001.00
maleharmWRSequential trials, ITTV3_random26.2615.5114.4810.750.231.032.310.950.001.00
maleharmWRSequential trials, ITTV4_within24.7415.5114.489.230.251.032.540.950.031.00
maleharmWRCCW, A vs B2V1_ignore29.3814.9914.4814.400.470.504.660.840.071.00
maleharmWRCCW, A vs B2V2_cluster29.3814.9914.4814.400.470.504.660.880.081.00
maleharmWRCCW, A vs B2V3_random24.9614.9914.489.980.530.505.340.880.491.00
maleharmWRCCW, A vs B2V4_within24.5414.9914.489.550.580.505.820.900.591.00
maleharmWRCCW, A vs B1 (reference)V1_ignore28.9814.4814.4814.490.460.004.640.840.071.00
maleharmWRCCW, A vs B1 (reference)V2_cluster28.9814.4814.4814.490.460.004.640.880.081.00
maleharmWRCCW, A vs B1 (reference)V3_random24.4614.4814.489.980.530.005.350.880.481.00
maleharmWRCCW, A vs B1 (reference)V4_within23.6014.4814.489.120.580.005.840.900.601.00
malenullW0Naive ‘ever early PVI’V1_ignore16.0216.1814.23-0.160.091.951.701.060.931.00
malenullW0Naive ‘ever early PVI’V2_cluster16.0216.1814.23-0.160.091.951.701.050.931.00
malenullW0Naive ‘ever early PVI’V3_random16.1316.1814.23-0.050.081.951.731.060.961.00
malenullW0Naive ‘ever early PVI’V4_within15.5016.1814.23-0.680.171.952.281.010.941.00
malenullW0Landmark at 6 monthsV1_ignore14.7914.6814.230.110.090.451.781.000.951.00
malenullW0Landmark at 6 monthsV2_cluster14.7914.6814.230.110.090.451.781.000.941.00
malenullW0Landmark at 6 monthsV3_random14.8614.6814.230.190.080.451.820.990.931.00
malenullW0Landmark at 6 monthsV4_within14.5914.6814.23-0.090.150.452.380.960.931.00
malenullW0Sequential trials, ITTV1_ignore15.0715.0614.230.010.170.841.691.010.961.00
malenullW0Sequential trials, ITTV2_cluster15.0715.0614.230.010.170.841.691.000.961.00
malenullW0Sequential trials, ITTV3_random15.0615.0614.23-0.000.170.841.750.990.941.00
malenullW0Sequential trials, ITTV4_within14.8415.0614.23-0.220.220.842.150.960.931.00
malenullW0CCW, A vs B2V1_ignore14.3214.4714.23-0.150.400.243.980.950.930.97
malenullW0CCW, A vs B2V2_cluster14.3214.4714.23-0.150.400.243.980.950.930.98
malenullW0CCW, A vs B2V3_random14.4914.4714.230.020.400.243.980.970.940.98
malenullW0CCW, A vs B2V4_within14.7014.4714.230.240.460.244.571.010.930.95
malenullW0CCW, A vs B1 (reference)V1_ignore14.0914.2314.23-0.140.400.003.980.950.930.97
malenullW0CCW, A vs B1 (reference)V2_cluster14.0914.2314.23-0.140.400.003.980.950.930.97
malenullW0CCW, A vs B1 (reference)V3_random14.2614.2314.230.030.400.003.980.970.940.97
malenullW0CCW, A vs B1 (reference)V4_within14.4514.2314.230.220.460.004.571.010.930.95
malenullW1Naive ‘ever early PVI’V1_ignore16.4515.6213.860.830.111.762.090.880.921.00
malenullW1Naive ‘ever early PVI’V2_cluster16.4515.6213.860.830.111.762.090.900.931.00
malenullW1Naive ‘ever early PVI’V3_random16.2815.6213.860.660.091.762.150.890.941.00
malenullW1Naive ‘ever early PVI’V4_within15.2515.6213.86-0.370.161.762.480.950.921.00
malenullW1Landmark at 6 monthsV1_ignore15.0413.9213.861.120.090.051.960.920.901.00
malenullW1Landmark at 6 monthsV2_cluster15.0413.9213.861.120.090.051.960.940.921.00
malenullW1Landmark at 6 monthsV3_random14.8013.9213.860.890.080.051.960.940.931.00
malenullW1Landmark at 6 monthsV4_within14.1013.9213.860.180.150.052.390.950.941.00
malenullW1Sequential trials, ITTV1_ignore15.4314.6213.860.800.190.761.920.900.911.00
malenullW1Sequential trials, ITTV2_cluster15.4314.6213.860.800.190.761.920.920.921.00
malenullW1Sequential trials, ITTV3_random15.1014.6213.860.470.200.761.960.910.951.00
malenullW1Sequential trials, ITTV4_within14.4714.6213.86-0.150.230.762.310.920.911.00
malenullW1CCW, A vs B2V1_ignore14.8614.1213.860.740.410.254.120.960.940.99
malenullW1CCW, A vs B2V2_cluster14.8614.1213.860.740.410.254.120.970.940.99
malenullW1CCW, A vs B2V3_random14.5114.1213.860.390.410.254.130.970.930.99
malenullW1CCW, A vs B2V4_within14.4314.1213.860.310.480.254.840.960.940.94
malenullW1CCW, A vs B1 (reference)V1_ignore14.6113.8613.860.750.410.004.120.960.940.99
malenullW1CCW, A vs B1 (reference)V2_cluster14.6113.8613.860.750.410.004.120.970.940.99
malenullW1CCW, A vs B1 (reference)V3_random14.2613.8613.860.400.410.004.130.970.920.99
malenullW1CCW, A vs B1 (reference)V4_within14.1513.8613.860.290.480.004.840.960.940.93
malenullW1_rho0Naive ‘ever early PVI’V1_ignore16.2916.3914.77-0.100.091.621.741.050.961.00
malenullW1_rho0Naive ‘ever early PVI’V2_cluster16.2916.3914.77-0.100.091.621.741.070.971.00
malenullW1_rho0Naive ‘ever early PVI’V3_random16.4416.3914.770.050.081.621.811.050.971.00
malenullW1_rho0Naive ‘ever early PVI’V4_within15.8816.3914.77-0.510.141.622.171.070.951.00
malenullW1_rho0Landmark at 6 monthsV1_ignore15.0114.9214.770.090.100.151.641.100.981.00
malenullW1_rho0Landmark at 6 monthsV2_cluster15.0114.9214.770.090.100.151.641.120.981.00
malenullW1_rho0Landmark at 6 monthsV3_random15.1114.9214.770.190.080.151.691.110.981.00
malenullW1_rho0Landmark at 6 monthsV4_within14.9014.9214.77-0.030.130.152.011.150.981.00
malenullW1_rho0Sequential trials, ITTV1_ignore15.3615.6214.77-0.260.170.841.651.050.981.00
malenullW1_rho0Sequential trials, ITTV2_cluster15.3615.6214.77-0.260.170.841.651.070.981.00
malenullW1_rho0Sequential trials, ITTV3_random15.3515.6214.77-0.270.170.841.711.050.971.00
malenullW1_rho0Sequential trials, ITTV4_within15.1615.6214.77-0.450.200.842.001.080.951.00
malenullW1_rho0CCW, A vs B2V1_ignore14.9915.0214.77-0.030.330.253.251.200.981.00
malenullW1_rho0CCW, A vs B2V2_cluster14.9915.0214.77-0.030.330.253.251.210.991.00
malenullW1_rho0CCW, A vs B2V3_random14.9515.0214.77-0.070.340.253.411.160.971.00
malenullW1_rho0CCW, A vs B2V4_within14.8315.0214.77-0.190.400.254.031.140.960.96
malenullW1_rho0CCW, A vs B1 (reference)V1_ignore14.7514.7714.77-0.020.330.003.251.200.981.00
malenullW1_rho0CCW, A vs B1 (reference)V2_cluster14.7514.7714.77-0.020.330.003.251.210.981.00
malenullW1_rho0CCW, A vs B1 (reference)V3_random14.7114.7714.77-0.060.340.003.411.160.970.98
malenullW1_rho0CCW, A vs B1 (reference)V4_within14.5714.7714.77-0.200.400.004.031.140.960.96
malenullW2Naive ‘ever early PVI’V1_ignore17.4326.1414.83-8.720.9911.321.841.020.021.00
malenullW2Naive ‘ever early PVI’V2_cluster17.4326.1414.83-8.720.9911.321.841.030.011.00
malenullW2Naive ‘ever early PVI’V3_random18.3826.1414.83-7.760.9711.322.921.060.261.00
malenullW2Naive ‘ever early PVI’V4_within15.8026.1414.83-10.341.0611.323.660.970.161.00
malenullW2Landmark at 6 monthsV1_ignore16.0516.0614.83-0.000.541.231.801.020.951.00
malenullW2Landmark at 6 monthsV2_cluster16.0516.0614.83-0.000.541.231.801.030.951.00
malenullW2Landmark at 6 monthsV3_random15.7216.0614.83-0.340.501.232.910.990.951.00
malenullW2Landmark at 6 monthsV4_within14.8216.0614.83-1.240.531.233.830.930.870.99
malenullW2Sequential trials, ITTV1_ignore16.3415.7414.830.600.170.911.731.020.941.00
malenullW2Sequential trials, ITTV2_cluster16.3415.7414.830.600.170.911.731.020.951.00
malenullW2Sequential trials, ITTV3_random15.8415.7414.830.100.250.912.540.960.961.00
malenullW2Sequential trials, ITTV4_within15.0415.7414.83-0.700.300.912.970.900.911.00
malenullW2CCW, A vs B2V1_ignore15.6515.1414.830.510.420.314.190.990.931.00
malenullW2CCW, A vs B2V2_cluster15.6515.1414.830.510.420.314.190.980.931.00
malenullW2CCW, A vs B2V3_random15.8015.1414.830.660.640.316.380.850.960.91
malenullW2CCW, A vs B2V4_within15.4215.1414.830.280.750.317.490.780.940.79
malenullW2CCW, A vs B1 (reference)V1_ignore15.3914.8314.830.570.420.004.190.990.931.00
malenullW2CCW, A vs B1 (reference)V2_cluster15.3914.8314.830.570.420.004.190.980.931.00
malenullW2CCW, A vs B1 (reference)V3_random15.5114.8314.830.680.640.006.380.850.960.91
malenullW2CCW, A vs B1 (reference)V4_within14.8914.8314.830.060.750.007.490.780.950.76
malenullW3Naive ‘ever early PVI’V1_ignore18.4617.597.970.880.719.622.030.930.911.00
malenullW3Naive ‘ever early PVI’V2_cluster18.4617.597.970.880.719.622.031.010.941.00
malenullW3Naive ‘ever early PVI’V3_random14.0417.597.97-3.550.679.622.840.950.700.99
malenullW3Naive ‘ever early PVI’V4_within10.7717.597.97-6.820.739.622.970.970.320.98
malenullW3Landmark at 6 monthsV1_ignore16.968.327.978.650.430.341.990.940.001.00
malenullW3Landmark at 6 monthsV2_cluster16.968.327.978.650.430.341.991.030.001.00
malenullW3Landmark at 6 monthsV3_random11.328.327.973.010.350.342.410.990.770.99
malenullW3Landmark at 6 monthsV4_within9.678.327.971.350.370.342.910.960.930.93
malenullW3Sequential trials, ITTV1_ignore17.398.277.979.120.200.292.010.890.001.00
malenullW3Sequential trials, ITTV2_cluster17.398.277.979.120.200.292.010.980.001.00
malenullW3Sequential trials, ITTV3_random11.548.277.973.280.230.292.320.940.731.00
malenullW3Sequential trials, ITTV4_within10.248.277.971.970.260.292.590.920.831.00
malenullW3CCW, A vs B2V1_ignore14.628.337.976.290.410.364.060.990.700.98
malenullW3CCW, A vs B2V2_cluster14.628.337.976.290.410.364.061.010.700.97
malenullW3CCW, A vs B2V3_random10.298.337.971.960.470.364.701.000.940.59
malenullW3CCW, A vs B2V4_within9.538.337.971.200.500.364.961.030.930.49
malenullW3CCW, A vs B1 (reference)V1_ignore14.347.977.976.370.410.004.060.990.680.98
malenullW3CCW, A vs B1 (reference)V2_cluster14.347.977.976.370.410.004.061.010.660.97
malenullW3CCW, A vs B1 (reference)V3_random9.957.977.971.980.470.004.691.000.940.59
malenullW3CCW, A vs B1 (reference)V4_within8.907.977.970.920.490.004.941.030.930.40
malenullW4Naive ‘ever early PVI’V1_ignore14.5214.9412.85-0.410.102.081.721.060.971.00
malenullW4Naive ‘ever early PVI’V2_cluster14.5214.9412.85-0.410.102.081.721.060.961.00
malenullW4Naive ‘ever early PVI’V3_random14.6914.9412.85-0.250.102.081.791.050.961.00
malenullW4Naive ‘ever early PVI’V4_within14.0214.9412.85-0.920.152.082.271.060.901.00
malenullW4Landmark at 6 monthsV1_ignore13.3313.3612.85-0.030.090.511.621.120.991.00
malenullW4Landmark at 6 monthsV2_cluster13.3313.3612.85-0.030.090.511.621.110.991.00
malenullW4Landmark at 6 monthsV3_random13.4513.3612.850.090.080.511.671.111.001.00
malenullW4Landmark at 6 monthsV4_within13.0613.3612.85-0.300.130.512.181.100.991.00
malenullW4Sequential trials, ITTV1_ignore13.6513.7112.85-0.070.160.861.611.070.971.00
malenullW4Sequential trials, ITTV2_cluster13.6513.7112.85-0.070.160.861.611.070.971.00
malenullW4Sequential trials, ITTV3_random13.6913.7112.85-0.020.160.861.641.080.981.00
malenullW4Sequential trials, ITTV4_within13.3913.7112.85-0.320.200.862.001.070.981.00
malenullW4CCW, A vs B2V1_ignore15.2514.4112.850.840.391.563.951.020.951.00
malenullW4CCW, A vs B2V2_cluster15.2514.4112.850.840.391.563.951.010.950.99
malenullW4CCW, A vs B2V3_random15.2914.4112.850.880.391.563.931.030.941.00
malenullW4CCW, A vs B2V4_within14.7914.4112.850.380.441.564.451.070.980.93
malenullW4CCW, A vs B1 (reference)V1_ignore13.7612.8512.850.910.390.003.941.020.950.98
malenullW4CCW, A vs B1 (reference)V2_cluster13.7612.8512.850.910.390.003.941.010.950.99
malenullW4CCW, A vs B1 (reference)V3_random13.7312.8512.850.880.390.003.931.030.940.99
malenullW4CCW, A vs B1 (reference)V4_within13.1912.8512.850.340.440.004.371.080.980.87
malenullW5Naive ‘ever early PVI’V1_ignore21.9716.0414.235.940.161.811.301.050.011.00
malenullW5Naive ‘ever early PVI’V2_cluster21.9716.0414.235.940.161.811.301.050.011.00
malenullW5Naive ‘ever early PVI’V3_random22.0616.0414.236.020.161.811.311.060.001.00
malenullW5Naive ‘ever early PVI’V4_within21.8516.0414.235.810.171.811.501.120.031.00
malenullW5Landmark at 6 monthsV1_ignore19.2814.3914.234.890.150.161.341.110.041.00
malenullW5Landmark at 6 monthsV2_cluster19.2814.3914.234.890.150.161.341.100.041.00
malenullW5Landmark at 6 monthsV3_random19.3414.3914.234.950.150.161.391.090.061.00
malenullW5Landmark at 6 monthsV4_within19.3514.3914.234.960.180.161.761.050.201.00
malenullW5Sequential trials, ITTV1_ignore20.2115.0614.235.140.130.841.301.080.031.00
malenullW5Sequential trials, ITTV2_cluster20.2115.0614.235.140.130.841.301.080.031.00
malenullW5Sequential trials, ITTV3_random20.2415.0614.235.170.130.841.301.110.031.00
malenullW5Sequential trials, ITTV4_within20.2315.0614.235.160.150.841.511.150.141.00
malenullW5CCW, A vs B2V1_ignore18.5714.4714.234.100.300.243.051.040.751.00
malenullW5CCW, A vs B2V2_cluster18.5714.4714.234.100.300.243.051.040.751.00
malenullW5CCW, A vs B2V3_random18.5514.4714.234.090.320.243.201.010.761.00
malenullW5CCW, A vs B2V4_within18.3614.4714.233.890.390.243.920.980.871.00
malenullW5CCW, A vs B1 (reference)V1_ignore18.3414.2314.234.110.310.003.051.040.751.00
malenullW5CCW, A vs B1 (reference)V2_cluster18.3414.2314.234.110.310.003.051.040.741.00
malenullW5CCW, A vs B1 (reference)V3_random18.3214.2314.234.100.320.003.211.000.761.00
malenullW5CCW, A vs B1 (reference)V4_within18.1114.2314.233.880.390.003.930.980.870.99
malenullWRNaive ‘ever early PVI’V1_ignore22.6916.386.686.310.799.702.090.720.061.00
malenullWRNaive ‘ever early PVI’V2_cluster22.6916.386.686.310.799.702.090.900.101.00
malenullWRNaive ‘ever early PVI’V3_random18.0516.386.681.670.769.702.160.890.831.00
malenullWRNaive ‘ever early PVI’V4_within16.0916.386.68-0.290.799.702.390.880.891.00
malenullWRLandmark at 6 monthsV1_ignore20.287.016.6813.270.470.332.240.720.001.00
malenullWRLandmark at 6 monthsV2_cluster20.287.016.6813.270.470.332.240.890.001.00
malenullWRLandmark at 6 monthsV3_random14.697.016.687.680.410.332.280.860.051.00
malenullWRLandmark at 6 monthsV4_within13.647.016.686.630.420.332.620.860.181.00
malenullWRSequential trials, ITTV1_ignore21.096.886.6814.210.220.202.160.710.001.00
malenullWRSequential trials, ITTV2_cluster21.096.886.6814.210.220.202.160.890.001.00
malenullWRSequential trials, ITTV3_random15.406.886.688.520.210.202.060.910.001.00
malenullWRSequential trials, ITTV4_within14.306.886.687.420.230.202.300.890.091.00
malenullWRCCW, A vs B2V1_ignore18.577.056.6811.520.390.373.900.920.101.00
malenullWRCCW, A vs B2V2_cluster18.577.056.6811.520.390.373.900.960.111.00
malenullWRCCW, A vs B2V3_random14.247.056.687.190.470.374.680.860.630.97
malenullWRCCW, A vs B2V4_within13.737.056.686.680.510.375.080.850.720.92
malenullWRCCW, A vs B1 (reference)V1_ignore18.266.686.6811.580.390.003.890.920.101.00
malenullWRCCW, A vs B1 (reference)V2_cluster18.266.686.6811.580.390.003.890.960.111.00
malenullWRCCW, A vs B1 (reference)V3_random13.866.686.687.180.470.004.680.860.630.97
malenullWRCCW, A vs B1 (reference)V4_within13.016.686.686.330.510.005.070.850.740.90
+
+ + + +
+
+ +
+ + + + + + + + + + + + + + + + diff --git a/simulations/04_summarise.R b/simulations/04_summarise.R new file mode 100644 index 0000000..7da92b7 --- /dev/null +++ b/simulations/04_summarise.R @@ -0,0 +1,90 @@ +# Performance against the known truths (plan sections 9 and 10). +# Usage: Rscript simulations/04_summarise.R [results_dir] +# Writes results/summary/{reps,perf,nc_reps,nc_summary}.csv. + +suppressPackageStartupMessages(library(data.table)) +a <- commandArgs(TRUE) +indir <- if (length(a) >= 1) a[1] else "results/main/n50k" +outdir <- "results/summary" +dir.create(outdir, recursive = TRUE, showWarnings = FALSE) + +files <- list.files(indir, pattern = "^rep[0-9]+\\.csv$", recursive = TRUE, full.names = TRUE) +d <- rbindlist(lapply(files, fread), fill = TRUE) +cat(sprintf("%d files, %d rows\n", length(files), nrow(d))) +# rare failures (sequential V4: fixed effects not estimable in a trial month) are dropped and counted +cat(sprintf("dropped %d estimates with NA\n", d[is.na(est), .N])) +d <- d[!is.na(est)] + +tr <- rbind(fread("simulations/truths/truths.csv"), fread("simulations/truths/truths_rct.csv")) +tr <- tr[, .(world, scenario, outcome, estimand, truth = value, truth_mcse = mcse)] + +# ---- own estimand and reference for every estimate ----------------------------------- +# reference (clinically relevant) = RD(A - B1); NCE has a true effect of exactly 0 (D32) +d[, estimand := fcase(design == "ccw" & contrast == "A_B1", "rd_A_B1", + design == "ccw" & contrast == "A_B2", "rd_A_B2", + design == "seq", "seq_itt", + design %like% "^rct_" & contrast == "itt", paste0(design, "_itt"), + default = NA_character_)] +d <- merge(d, tr[, .(world, scenario, outcome, estimand, own = truth)], + by = c("world", "scenario", "outcome", "estimand"), all.x = TRUE) +d <- merge(d, tr[estimand == "rd_A_B1", .(world, scenario, outcome, ref = truth)], + by = c("world", "scenario", "outcome"), all.x = TRUE) +# landmark and naive: own estimand = the paired oracle estimate in the same replicate (D47) +orc <- d[variant == "oracle", .(world, scenario, rep, design, outcome, oracle = est)] +d <- merge(d, orc, by = c("world", "scenario", "rep", "design", "outcome"), all.x = TRUE) +d[design %in% c("landmark", "naive") & variant != "oracle", own := oracle] +d[analysis %like% "^nce", `:=`(own = 0, ref = 0)] +d[, oracle := NULL] +d[, `:=`(lo = est - 1.96 * se, hi = est + 1.96 * se)] +fwrite(d, file.path(outdir, "reps.csv")) + +# ---- performance by cell (plan 9) ----------------------------------------------------- +keys <- c("world", "scenario", "analysis", "design", "contrast", "variant", "outcome") +perf <- d[variant != "oracle", { + err <- est - own; R <- .N; own_m <- mean(own) + .(reps = R, mean_est = mean(est), own = own_m, ref = ref[1], + bias = mean(err), mcse_bias = sd(err) / sqrt(R), + gap = own_m - ref[1], total = mean(est) - ref[1], + emp_se = sd(est), mod_se = sqrt(mean(se^2)), + se_ratio = sqrt(mean(se^2)) / sd(est), + cover_own = mean(lo <= own_m & hi >= own_m), + cover_ref = mean(lo <= ref[1] & hi >= ref[1]), + reject0 = mean(lo > 0 | hi < 0), + rmse_own = sqrt(mean((est - own_m)^2)), rmse_ref = sqrt(mean((est - ref[1])^2))) +}, by = keys] +perf[, `:=`(mcse_cover = sqrt(cover_own * (1 - cover_own) / reps), + mcse_reject = sqrt(reject0 * (1 - reject0) / reps))] +fwrite(perf, file.path(outdir, "perf.csv")) + +# ---- negative controls as bias detectors (plan 10) ------------------------------------ +# One unit = world x scenario x design x variant x replicate. Bias is in the MALE RD +# against the method's own estimand; a control "flags" when its 95% CI excludes 0. +m <- d[analysis == "main" & variant %in% c("V1_ignore", "V2_cluster", "V3_random", "V4_within") & + contrast %in% c("early_none", "A_B1", "now_not")] +u <- c("world", "scenario", "design", "variant", "rep") +flag <- function(x) x$lo > 0 | x$hi < 0 +nc <- m[outcome == "male", c(..u, "est", "own")][, err := est - own] +nc <- merge(nc, m[outcome == "nco1", c(..u)][, nco1 := flag(m[outcome == "nco1"])], by = u, all.x = TRUE) +nc <- merge(nc, m[outcome == "nco2", c(..u)][, nco2 := flag(m[outcome == "nco2"])], by = u, all.x = TRUE) +ne <- d[analysis == "nce" & outcome == "male" & contrast %in% c("early_none", "A_B1")] +nc <- merge(nc, ne[, c(..u)][, nce := flag(ne)], by = u, all.x = TRUE) +nc[, any_nc := nco1 | nco2 | fifelse(is.na(nce), FALSE, nce)] +nc[, cell_bias := mean(err), by = .(world, scenario, design, variant)] +nc[, `:=`(biased_rep = abs(err) > 0.01, biased_cell = abs(cell_bias) > 0.01)] +fwrite(nc, file.path(outdir, "nc_reps.csv")) + +acc <- function(x, truth) { + ok <- !is.na(x) + list(sens = mean(x[ok & truth]), spec = mean(!x[ok & !truth]), + n_biased = sum(ok & truth), n_clean = sum(ok & !truth)) +} +ncs <- rbindlist(lapply(c("nco1", "nco2", "nce", "any_nc"), function(k) rbind( + nc[, c(control = k, level = "replicate", acc(get(k), biased_rep)), by = world], + nc[, c(control = k, level = "cell", acc(get(k), biased_cell)), by = world], + nc[, c(world = "all", control = k, level = "replicate", acc(get(k), biased_rep))], + nc[, c(world = "all", control = k, level = "cell", acc(get(k), biased_cell))]))) +hide <- nc[any_nc == FALSE, .(control = "any_nc", med_absbias_clean = median(abs(cell_bias)), + p90_absbias_clean = quantile(abs(cell_bias), 0.9)), by = world] +ncs <- merge(ncs, hide, by = c("world", "control"), all.x = TRUE) +fwrite(ncs, file.path(outdir, "nc_summary.csv")) +cat("wrote", outdir, "\n")