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D56: limb MALE (CLTI, major amputation, open bypass) as primary outcome; MALE incl. repeat revascularization to supplement

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Co-Authored-By: Claude Opus 5.5 <[email protected]>
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chendun1995
2026-10-28 16:39:59 -04:00
co-authored by Claude Opus 5.5
parent d369a16208
commit 3aa6c1c5b3
12 changed files with 37 additions and 33 deletions
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reports/draft-report.Rmd
+18 -18
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@@ -52,7 +52,7 @@ perf[, vlab := factor(VAR[variant], levels = VAR)]
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)**,
* **Outcome shown.** The headline outcome is **limb MALE (CLTI, major amputation or open bypass; D56)**,
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.
@@ -64,7 +64,7 @@ perf[, vlab := factor(VAR[variant], levels = VAR)]
# 1. Aim 1: what each method answers (clean world W0)
```{r tab2}
t2 <- perf[world == "W0" & outcome == "disease" & variant %in% c("V2_cluster", "none") &
t2 <- perf[world == "W0" & outcome == "limb" & 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),
@@ -89,7 +89,7 @@ knitr::kable(t2[order(Scenario, Method)], align = "llrrrrrrr",
the 5-point harm in only 25% of replicates.
```{r fig1, fig.width = 11, fig.height = 9}
ladder <- function(sc, oc = "disease") {
ladder <- function(sc, oc = "limb") {
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)]
@@ -109,24 +109,24 @@ ladder <- function(sc, oc = "disease") {
"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)")
ladder("harm") + ggtitle("Figure 1a. Estimand ladder, harm scenario (limb MALE)")
```
```{r fig1b, fig.width = 11, fig.height = 9}
ladder("null") + ggtitle("Figure 1b. Estimand ladder, null scenario (disease composite)")
ladder("null") + ggtitle("Figure 1b. Estimand ladder, null scenario (limb MALE)")
```
# 2. Aim 2: surgery-specific threats
```{r tab3}
x <- perf[scenario == "harm" & outcome == "disease" & analysis == "main" &
x <- perf[scenario == "harm" & outcome == "limb" & 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."))
"Harm scenario, limb MALE."))
```
```{r tab3b}
@@ -135,7 +135,7 @@ 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."))
"Harm scenario, limb MALE."))
```
**What it shows.**
@@ -157,28 +157,28 @@ knitr::kable(t3b[order(-as.integer(Method))], caption = paste(
# 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 <- perf[scenario == "null" & outcome == "limb" & 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%)") +
labs(x = NULL, y = NULL, title = "Figure 2. False-alarm rate, null scenario (limb MALE; 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"))]
outcome %in% c("male", "limb") & !grepl("as_treated", key) & key != "ccw|A_B2"]
m0[, oc := factor(ifelse(outcome == "male", "MALE incl. repeat revascularization", "Limb MALE"), levels = c("Limb MALE", "MALE incl. repeat revascularization"))]
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) +
geom_point(size = 2, position = position_dodge(width = 0.5)) + scale_colour_manual(values = c("Limb MALE" = "#2a78d6", "MALE incl. repeat revascularization" = "#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")
title = "Figure 2 inset. Sharp null: limb MALE 0, yet MALE incl. repeat revascularization about +14 pp")
```
**What it shows.**
@@ -242,10 +242,10 @@ ggplot(cb, aes(wlab, 100 * absbias, colour = flag)) +
# 5. Simulated RCTs
```{r tabrct}
r <- perf[analysis == "rct" & outcome == "disease" & scenario != "null"]
r <- perf[analysis == "rct" & outcome == "limb" & 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).")
knitr::kable(r[order(World, Scenario, Trial)], caption = "Table 5. Simulated trials, limb MALE (pp).")
```
* **Surgeon selection changes the answer.** In WR, a realistic trial recruits hospital-based,
@@ -291,7 +291,7 @@ if (length(bf)) {
x <- bt[, .(reps = .N, boot = sqrt(mean(boot_se^2)), patient = sqrt(mean(se_patient^2)),
cluster = sqrt(mean(se_cluster^2))), by = .(world, scenario, design, contrast, outcome)]
x <- merge(x, emp, by = c("world", "scenario", "design", "contrast", "outcome"))
x <- x[outcome == "disease" & contrast != "A_B2"]
x <- x[outcome == "limb" & contrast != "A_B2"]
knitr::kable(x[order(world, scenario, design), .(World = world, Scenario = scenario, Design = design,
`Empirical SE (100 reps)` = pp(emp_se, 2), `Bootstrap SE` = pp(boot, 2), `Model SE, cluster` = pp(cluster, 2),
`Model SE, patient` = pp(patient, 2), `Bootstrap / empirical` = round(boot / emp_se, 2),
@@ -303,7 +303,7 @@ if (length(bf)) {
# Supplement: full performance table
```{r supp}
x <- perf[analysis == "main" & outcome %in% c("disease", "male")]
x <- perf[analysis == "main" & outcome %in% c("limb", "male", "disease")]
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),