Speed-ups (shared PVI fit, clones once, case-control sampled deferred model); final scale 200 reps extending the draft (D51)

Co-Authored-By: Claude Opus 5.5 <[email protected]>

docs/decisions.md

@@ -102,3 +102,4 @@ Each decision is listed with the date and the reason for it. Settled decisions a
 | 48 | **The PVI model is fitted separately inside the window (month indicators) and in the deferred period (spline in month).** | A pooled model misspecified the window probabilities that the CCW and sequential weights depend on. |
 | 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%. |

pvisim/R/estimators.R

@@ -51,31 +51,45 @@ fit_pvi <- function(data, newdata, variant, extra_covs = NULL) {
     d <- data[win == w]
     nd <- newdata[i_new]
     tt_levels <- if (w == 1L) "factor(tt)" else "splines::ns(t, df = 4)"
+    # deferred period (about 2 million person-months, few PVIs): fit on every PVI and a
+    # fixed 10% sample of the other months, with offset log(1/0.1) so the coefficients
+    # are those of the full model (case-control sampling of person-months)
+    if (w == 0L) d <- sample_controls(d, frac = 0.1)
     # no exposure at all in a phase (the NCE after the window): probability 0
     out[i_new] <- if (sum(d$pvi) == 0) 0 else fit_pvi_phase(d, nd, variant, tt_levels, extra_covs)
   }
   out
 }
 
+#' Keep all events and a deterministic fraction of non-events; offset column `off`.
+sample_controls <- function(d, frac) {
+  keep <- d$pvi == 1L | (((d$id * 7919L + d$t * 104729L) %% 1000L) < frac * 1000)
+  d <- d[keep]
+  d[, off := log(1 / frac)]   # sampling raises every row's odds by 1/frac
+  d
+}
+
 fit_pvi_phase <- function(d, nd, variant, time_terms, extra_covs = NULL) {
+  if (!"off" %in% names(d)) d[, off := 0]
+  nd <- copy(nd)[, off := 0]                 # predictions are for the full population
   covs <- c(BASE_COVS, "sev_obs", "sev0", extra_covs)
   if (variant == "V4_within") covs <- setdiff(covs, "obl")
   rhs <- paste(c(covs, time_terms), collapse = " + ")
   if (variant %in% c("V1_ignore", "V2_cluster")) {
     f <- as.formula(paste("~", rhs))
     X <- model.matrix(f, d)
-    fit <- fastglm::fastglm(X, d$pvi, family = binomial(), method = 2)
+    fit <- fastglm::fastglm(X, d$pvi, family = binomial(), method = 2, offset = d$off)
     b <- coef(fit); b[is.na(b)] <- 0
     Xn <- model.matrix(f, nd)
     return(as.vector(plogis(Xn %*% b)))
   }
   if (variant == "V3_random") {
-    fit <- lme4::glmer(as.formula(paste("pvi ~", rhs, "+ (1 | phys_id)")), data = d,
+    fit <- lme4::glmer(as.formula(paste("pvi ~", rhs, "+ offset(off) + (1 | phys_id)")), data = d,
                        family = binomial(), nAGQ = 0, control = lme4::glmerControl(calc.derivs = FALSE))
     return(as.vector(predict(fit, newdata = nd, type = "response", allow.new.levels = TRUE)))
   }
   if (variant == "V4_within") {
-    fit <- fixest::feglm(as.formula(paste("pvi ~", rhs, "| phys_id")), data = d,
+    fit <- fixest::feglm(as.formula(paste("pvi ~", rhs, "| phys_id")), data = d, offset = ~off,
                          family = binomial(), notes = FALSE, warn = FALSE)
     pr <- as.vector(predict(fit, newdata = nd, type = "response"))
     pr[is.na(pr)] <- 0
@@ -162,22 +176,33 @@ restrict_variant <- function(rec, variant) {
 #' patient-level for V1, physician-cluster-robust for V2-V4.
 run_design <- function(rec, design, variant, outcomes = c(male = "t_male", disease = "t_disease",
                                                            nco1 = "t_nco1", nco2 = "t_nco2")) {
+  run_designs(rec, design, variant, outcomes)
+}
+
+#' Several designs under one variant, sharing the PVI fit between CCW and sequential
+#' trials (the sequential propensity score is the window part of the same model).
+run_designs <- function(rec, designs, variant, outcomes = c(male = "t_male", disease = "t_disease",
+                                                             nco1 = "t_nco1", nco2 = "t_nco2")) {
   rec <- restrict_variant(rec, variant)
-  rs <- if (design %in% c("ccw", "seq")) pvi_risk_set(rec) else NULL
+  need <- intersect(designs, c("ccw", "seq"))
-  phat <- if (design == "ccw") fit_pvi(rs, rs, variant, rec$extra_covs) else NULL
+  rs <- if (length(need)) pvi_risk_set(rec) else NULL
+  phat <- if ("ccw" %in% need) fit_pvi(rs, rs, variant, rec$extra_covs)
+          else if ("seq" %in% need) { x <- rep(NA_real_, nrow(rs)); w <- rs$win == 1L
+                                      x[w] <- fit_pvi(rs[w], rs[w], variant, rec$extra_covs); x }
   cmp <- if (is.null(rec$comparators)) c("B1", "B2") else rec$comparators
-  out <- lapply(names(outcomes), function(o) {
-    r <- switch(design,
-      naive = cbind(contrast = "early_none", est_naive(rec, outcomes[[o]], variant)),
-      landmark = cbind(contrast = "early_none", est_landmark(rec, outcomes[[o]], variant, extra_covs = rec$extra_covs)),
-      ccw = est_ccw(rec, outcomes[[o]], variant, rs = copy(rs), phat = phat, comparators = cmp),
-      seq = cbind(contrast = "now_not", est_seq(rec, outcomes[[o]], variant, rs = copy(rs))))
-    r[, `:=`(outcome = o, design = design, variant = variant, n = nrow(rec$patients))]
   use_patient <- variant == "V1_ignore"
+  rbindlist(lapply(designs, function(design) {
+    r <- switch(design,
+      naive = rbindlist(lapply(names(outcomes), function(o)
+        cbind(contrast = "early_none", est_naive(rec, outcomes[[o]], variant), outcome = o))),
+      landmark = rbindlist(lapply(names(outcomes), function(o)
+        cbind(contrast = "early_none", est_landmark(rec, outcomes[[o]], variant, extra_covs = rec$extra_covs), outcome = o))),
+      ccw = est_ccw(rec, outcomes, variant, rs = copy(rs), phat = phat, comparators = cmp),
+      seq = cbind(contrast = "now_not", est_seq(rec, outcomes, variant, rs = copy(rs), phat = phat)))
+    r[, `:=`(design = design, variant = variant, n = nrow(rec$patients))]
     r[, se := if (use_patient) se_patient else se_cluster]
     r
-  })
+  }), fill = TRUE)
-  rbindlist(out, fill = TRUE)
 }
 
 #' Naive "ever early PVI" design: exposure = PVI in months 0..W-1, follow-up from month 0,
@@ -237,13 +262,14 @@ est_ccw <- function(rec, outcome = "t_male", variant = "V1_ignore", horizon = 60
                     comparators = c("B1", "B2"), rs = NULL, phat = NULL) {
   W <- rec$par$size$window
   if (is.null(rs)) rs <- pvi_risk_set(rec)
-  if (is.null(phat)) phat <- fit_pvi(rs, rs, variant)
+  if (is.null(phat)) phat <- fit_pvi(rs, rs, variant, rec$extra_covs)
   rs[, ph := phat]
   rs <- rs[!is.na(ph)]
   # everyone eligible at diagnosis is cloned, including patients with a MALE event or
-  # death in month 0 (events in the grace period count in both arms)
+  # death in month 0 (events in the grace period count in both arms). Clones and weights
+  # are built once over follow-up to death or the horizon; each outcome then truncates.
   p <- copy(rec$patients)
-  p[, t_out := get(outcome)]
+  p[, t_out := NA_integer_]
   base <- expand_followup(p, 0L, horizon)
   # P(PVI) for each eligible month; months where a patient is not at risk of PVI have
   # probability 0 of deviating
@@ -251,7 +277,7 @@ est_ccw <- function(rec, outcome = "t_male", variant = "V1_ignore", horizon = 60
   base[is.na(ph), ph := 0]
   base[, `:=`(tp = fifelse(is.na(t_pvi), Inf, as.numeric(t_pvi)), el = pvi_elective %in% TRUE)]
 
-  out <- list()
+  arms <- list()
   # arm A: censored after month W-1 if no PVI by then (and still at risk of PVI then)
   a <- copy(base)[, arm := "A"]
   cens_A <- a[t == W - 1L & ph > 0 & tp > W - 1L, id]          # deviated at month W-1
@@ -259,38 +285,47 @@ est_ccw <- function(rec, outcome = "t_male", variant = "V1_ignore", horizon = 60
   pA <- base[t == W - 1L & ph > 0, .(id, pA = ph)]
   a <- pA[a, on = "id"]
   a[, w := ifelse(t > W - 1L & !is.na(pA) & tp == W - 1L, 1 / pA, 1)]
-  out$A <- a[, .(id, phys_id, arm, t, w, ev, dth)]
+  arms$A <- a[, .(id, phys_id, arm, t, w)]
   for (cmp in comparators) {
     b <- copy(base)[, arm := cmp]
     last <- if (cmp == "B1") W - 1L else horizon
     b <- b[!(el & tp <= last & t > tp)]                         # censored after month tp
     b[, q := ifelse(t <= last, 1 - ph, 1)]
     b[, w := 1 / shift(cumprod(q), fill = 1), by = id]
-    out[[cmp]] <- b[, .(id, phys_id, arm, t, w, ev, dth)]
+    arms[[cmp]] <- b[, .(id, phys_id, arm, t, w)]
   }
-  aj <- aj_cif(rbindlist(out), horizon)
+  clones <- rbindlist(arms)
-  rbindlist(lapply(comparators, function(cmp) cbind(contrast = paste0("A_", cmp), rd_from_aj(aj, "A", cmp))))
+  ev_times <- rec$patients[, c("id", "t_death", unname(outcome)), with = FALSE]
+  rbindlist(lapply(seq_along(outcome), function(k) {
+    d <- ev_times[, .(id, t_death, t_out = get(outcome[k]))][clones, on = "id"]
+    d <- d[is.na(t_out) | t <= t_out]
+    aj <- aj_cif(mark_events(d), horizon)
+    rbindlist(lapply(comparators, function(cmp)
+      cbind(contrast = paste0("A_", cmp), rd_from_aj(aj, "A", cmp))))[
+        , outcome := if (is.null(names(outcome))) outcome[k] else names(outcome)[k]]
+  }))
 }
 
 #' Sequential trials, ITT (D14): a trial at each month m = 0..W-1 among patients eligible
 #' for PVI at m; treatment = PVI at m; propensity score from covariates at m; outcome
 #' counted in months m+1..horizon-1; trial RDs pooled with weights = number eligible.
-est_seq <- function(rec, outcome = "t_male", variant = "V1_ignore", horizon = 60L, rs = NULL) {
+est_seq <- function(rec, outcome = "t_male", variant = "V1_ignore", horizon = 60L, rs = NULL,
+                    phat = NULL) {
   W <- rec$par$size$window
   if (is.null(rs)) rs <- pvi_risk_set(rec)
+  if (!is.null(phat)) rs[, ps := phat]
   trials <- rs[t < W]
-  trials[, tt := t]                       # trial-month indicators in the PS model
+  # the trial-baseline propensity score is the window model of the PVI fit
-  ps <- fit_pvi(trials, trials, variant)
+  if (is.null(phat)) trials[, ps := fit_pvi(trials, trials, variant, rec$extra_covs)]
-  trials[, ps := ps]
   trials <- trials[!is.na(ps)]
   trials[, `:=`(m = t, arm = ifelse(pvi == 1L, "pvi", "no"),
                 w = ifelse(pvi == 1L, 1 / ps, 1 / (1 - ps)))]
-  p <- rec$patients[, .(id, t_out = get(outcome))]
+  trials <- trials[, .(id, phys_id, m, arm, w, t_death)]
-  trials <- p[trials, on = "id"]
+  rbindlist(lapply(seq_along(outcome), function(k) {
-  trials <- trials[is.na(t_out) | t_out > m]
+    tr_k <- rec$patients[, .(id, t_out = get(outcome[k]))][trials, on = "id"][is.na(t_out) | t_out > m]
     res <- list(); infs <- list()
     for (mm in 0:(W - 1)) {
-    tr <- trials[m == mm]
+      tr <- tr_k[m == mm]
       d <- expand_followup(tr[, .(id, phys_id, arm, w, t_out, t_death)], mm + 1L, horizon)
       aj <- aj_cif(d, horizon)
       x <- dcast(aj$inf, id + phys_id ~ arm, value.var = "inf", fill = 0)
@@ -302,7 +337,9 @@ est_seq <- function(rec, outcome = "t_male", variant = "V1_ignore", horizon = 60
     infs <- res[, .(m, wt)][infs, on = "m"]
     pi <- infs[, .(inf = sum(wt * inf)), by = .(id, phys_id)]
     data.table(est = sum(res$wt * res$est), se_patient = sqrt(sum(pi$inf^2)),
-             se_cluster = sqrt(sum(tapply(pi$inf, pi$phys_id, sum)^2)), risk1 = NA_real_, risk0 = NA_real_)
+               se_cluster = sqrt(sum(tapply(pi$inf, pi$phys_id, sum)^2)), risk1 = NA_real_, risk0 = NA_real_,
+               outcome = if (is.null(names(outcome))) outcome[k] else names(outcome)[k])
+  }))
 }
 
 #' Negative-control exposure (D32, D36): the early low-value procedure (NCE, months

simulations/config/experiments.yml

@@ -8,5 +8,5 @@ main:
   rct: [realistic, ideal]
 scales:
   smoke: {n_patients: 20000, reps: 1}
-  draft: {n_patients: 50000, reps: 100}
+  draft: {n_patients: 50000, reps: 100, dir: n50k}
-  final: {n_patients: 50000, reps: 500}                     # to be revisited after the draft (sample size, D25/D29)
+  final: {n_patients: 50000, reps: 200, dir: n50k}   # extends the draft's replicates 1-100 to 200 (D51)

simulations/run_experiment.R

@@ -10,7 +10,7 @@ cores <- as.integer(opt("--cores", "10"))
 cfg <- yaml::read_yaml("simulations/config/experiments.yml")
 g <- cfg[[grid_name]]; sc <- cfg$scales[[scale]]
 worlds <- if (is.null(opt("--worlds", NULL))) g$worlds else strsplit(opt("--worlds", NULL), ",")[[1]]
-outdir <- file.path("results", grid_name, scale)
+outdir <- file.path("results", grid_name, if (is.null(sc$dir)) scale else sc$dir)
 jobs <- CJ(world = worlds, scenario = unlist(g$scenarios), rep = seq_len(sc$reps), sorted = FALSE)
 jobs[, file := file.path(outdir, world, scenario, sprintf("rep%03d.csv", rep))]
 todo <- jobs[!file.exists(file)]
@@ -21,25 +21,17 @@ one_job <- function(i) {
   par <- load_params(j$world, j$scenario)
   rec <- simulate_records(par, rep = j$rep, n = sc$n_patients)
   out <- list()
+  with_v2 <- function(r) if (r$variant[1] == "V1_ignore")   # same fit, two SEs
+    rbind(r, copy(r)[, `:=`(variant = "V2_cluster", se = se_cluster)]) else r
+  nce_out <- c(male = "t_male", disease = "t_disease")
   for (v in g$variants) {
-    for (d in g$designs) {
+    out[[length(out) + 1]] <- with_v2(run_designs(rec, unlist(g$designs), v))[, analysis := "main"]
-      r <- run_design(rec, d, v)
+    if (isTRUE(g$nce))
-      if (v == "V1_ignore") {   # same fit, two SEs
+      out[[length(out) + 1]] <- with_v2(run_designs(nce_records(rec), c("landmark", "ccw"), v, nce_out))[, analysis := "nce"]
-        r2 <- copy(r)[, `:=`(variant = "V2_cluster", se = se_cluster)]
-        r <- rbind(r, r2)
-      }
-      out[[length(out) + 1]] <- r[, analysis := "main"]
-    }
-    if (isTRUE(g$nce)) for (d in c("landmark", "ccw")) {
-      r <- run_design(nce_records(rec), d, v, outcomes = c(male = "t_male", disease = "t_disease"))
-      if (v == "V1_ignore") r <- rbind(r, copy(r)[, `:=`(variant = "V2_cluster", se = se_cluster)])
-      out[[length(out) + 1]] <- r[, analysis := "nce"]
-    }
-  }
-  for (d in c("landmark", "ccw")) {     # unadjusted NCE (supplement, D36), V1/V2 only
-    r <- run_design(nce_records(rec, adjust_pvi = FALSE), d, "V1_ignore", outcomes = c(male = "t_male", disease = "t_disease"))
-    out[[length(out) + 1]] <- r[, analysis := "nce_unadjusted"]
   }
+  # unadjusted NCE (supplement, D36), V1/V2 only
+  out[[length(out) + 1]] <- with_v2(run_designs(nce_records(rec, adjust_pvi = FALSE), c("landmark", "ccw"),
+                                                "V1_ignore", nce_out))[, analysis := "nce_unadjusted"]
   out[[length(out) + 1]] <- run_oracle(rec)[, analysis := "main"]   # own estimand of landmark / naive
   for (dz in g$rct) {
     r <- run_rct(par, dz, rep = j$rep)

simulations/truths/truths_rct.csv

@@ -0,0 +1,193 @@
+world,scenario,outcome,estimand,value,mcse,n
+W0,null,male,rct_realistic_itt,0.135990258102635,0.000920184352771069,138782
+W0,null,disease,rct_realistic_itt,0,0,138782
+W0,null,nco1,rct_realistic_itt,0,0,138782
+W0,null,nco2,rct_realistic_itt,0,0,138782
+W0,null,male,rct_ideal_itt,0.132398000000003,0.000338976229994509,1000000
+W0,null,disease,rct_ideal_itt,0,0,1000000
+W0,null,nco1,rct_ideal_itt,0,0,1000000
+W0,null,nco2,rct_ideal_itt,0,0,1000000
+W0,harm,male,rct_realistic_itt,0.201618365494084,0.00108867376068166,138782
+W0,harm,disease,rct_realistic_itt,0.0529679641452063,0.00062217229612646,138782
+W0,harm,nco1,rct_realistic_itt,-0.00116729835281267,9.49963511299982e-05,138782
+W0,harm,nco2,rct_realistic_itt,-0.000187344180080983,4.20123301701807e-05,138782
+W0,harm,male,rct_ideal_itt,0.196825000000004,0.000401807268257721,1000000
+W0,harm,disease,rct_ideal_itt,0.0515179999999988,0.000228184021667706,1000000
+W0,harm,nco1,rct_ideal_itt,-0.000888999999999641,3.17523335710502e-05,1000000
+W0,harm,nco2,rct_ideal_itt,-0.000446999999999933,2.24232177395626e-05,1000000
+W0,benefit_harm,male,rct_realistic_itt,0.176492628727068,0.00109872534635429,138782
+W0,benefit_harm,disease,rct_realistic_itt,0.0389243561845205,0.000655419127377042,138782
+W0,benefit_harm,nco1,rct_realistic_itt,0.000677321266446925,0.000119259570456732,138782
+W0,benefit_harm,nco2,rct_realistic_itt,0.000244988543182881,5.48720558616628e-05,138782
+W0,benefit_harm,male,rct_ideal_itt,0.173512000000003,0.000406167146413866,1000000
+W0,benefit_harm,disease,rct_ideal_itt,0.0386590000000027,0.000241330768776287,1000000
+W0,benefit_harm,nco1,rct_ideal_itt,0.000763999999999833,4.10781936238178e-05,1000000
+W0,benefit_harm,nco2,rct_ideal_itt,0.000396000000000213,2.91177614495428e-05,1000000
+W1,null,male,rct_realistic_itt,0.0659595624792839,0.000666357827592477,138782
+W1,null,disease,rct_realistic_itt,0,0,138782
+W1,null,nco1,rct_realistic_itt,0,0,138782
+W1,null,nco2,rct_realistic_itt,0,0,138782
+W1,null,male,rct_ideal_itt,0.128842000000002,0.000335081857633484,1000000
+W1,null,disease,rct_ideal_itt,0,0,1000000
+W1,null,nco1,rct_ideal_itt,0,0,1000000
+W1,null,nco2,rct_ideal_itt,0,0,1000000
+W1,harm,male,rct_realistic_itt,0.0928002190485794,0.000795614703095957,138782
+W1,harm,disease,rct_realistic_itt,0.020910492715194,0.000413885769615974,138782
+W1,harm,nco1,rct_realistic_itt,-0.000439538268651489,6.64216071074703e-05,138782
+W1,harm,nco2,rct_realistic_itt,-2.8822181550956e-05,2.88221815509174e-05,138782
+W1,harm,male,rct_ideal_itt,0.204204999999998,0.000407803242113363,1000000
+W1,harm,disease,rct_ideal_itt,0.0726610000000019,0.000266656052216873,1000000
+W1,harm,nco1,rct_ideal_itt,-0.00116899999999966,3.61888760346696e-05,1000000
+W1,harm,nco2,rct_ideal_itt,-0.000669999999999987,2.67123906369683e-05,1000000
+W1,benefit_harm,male,rct_realistic_itt,0.0429234338747105,0.000826265889068951,138782
+W1,benefit_harm,disease,rct_realistic_itt,-0.0139859635975853,0.000473809643164687,138782
+W1,benefit_harm,nco1,rct_realistic_itt,0.00198152498162617,0.000124064597708836,138782
+W1,benefit_harm,nco2,rct_realistic_itt,0.000403510541712834,5.48653056966601e-05,138782
+W1,benefit_harm,male,rct_ideal_itt,0.194897999999999,0.000430045293588994,1000000
+W1,benefit_harm,disease,rct_ideal_itt,0.0869099999999998,0.000323927085666702,1000000
+W1,benefit_harm,nco1,rct_ideal_itt,0.000186000000000347,4.90098745761233e-05,1000000
+W1,benefit_harm,nco2,rct_ideal_itt,-7.20000000001851e-05,3.59443473717217e-05,1000000
+W1_rho0,null,male,rct_realistic_itt,0.0719694829047751,0.000793152060137895,106170
+W1_rho0,null,disease,rct_realistic_itt,0,0,106170
+W1_rho0,null,nco1,rct_realistic_itt,0,0,106170
+W1_rho0,null,nco2,rct_realistic_itt,0,0,106170
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simulations/validate_designs.R

@@ -15,7 +15,7 @@ par <- load_params(world, scenario)
 res <- rbindlist(parallel::mclapply(seq_len(reps), function(r) {
   rec <- simulate_records(par, rep = 1000L + r, n = n)
   rbindlist(lapply(c("naive", "landmark", "ccw", "seq"), function(d)
-    cbind(rep = r, run_design(rec, d, "V1_ignore", outcomes = c(male = "t_male", disease = "t_disease", nco1 = "t_nco1")))),
+    cbind(rep = r, run_designs(rec, d, "V1_ignore", outcomes = c(male = "t_male", disease = "t_disease", nco1 = "t_nco1")))),
     fill = TRUE)
 }, mc.cores = cores), fill = TRUE)