Truths, estimators (naive, landmark, CCW, sequential; physician variants V1-V4) and simulated RCTs

- truths.R: interventional truths by common random numbers (A, B1, B2, sequential ITT)
- estimators.R: weighted Aalen-Johansen with influence-function SEs (patient / physician cluster)
- rct.R: realistic and ideal trials, ITT and as-treated; RCT ITT truths
- scenario effects tuned in W0: harm +5.1 pp, benefit-then-harm -1.2 pp at 24 mo, +3.1 at 60

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

simulations/validate_designs.R

@@ -0,0 +1,33 @@
+# Validation (plan section 15): in a clean world, each design averaged over replicates
+# should match its own truth within Monte Carlo error.
+# Usage: Rscript simulations/validate_designs.R [world] [scenario] [reps] [cores] [n]
+
+pkgload::load_all("pvisim", quiet = TRUE)
+suppressPackageStartupMessages(library(data.table))
+a <- commandArgs(TRUE)
+world <- if (length(a) >= 1) a[1] else "W0"
+scenario <- if (length(a) >= 2) a[2] else "harm"
+reps <- if (length(a) >= 3) as.integer(a[3]) else 40L
+cores <- if (length(a) >= 4) as.integer(a[4]) else 10L
+n <- if (length(a) >= 5) as.numeric(a[5]) else 5e4
+
+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")))),
+    fill = TRUE)
+}, mc.cores = cores), fill = TRUE)
+
+tr <- fread("simulations/truths/truths.csv")[world == ..world & scenario == ..scenario]
+map <- data.table(design = c("ccw", "ccw", "seq"), contrast = c("A_B1", "A_B2", "now_not"),
+                  estimand = c("rd_A_B1", "rd_A_B2", "seq_itt"))
+s <- res[, .(mean_est = mean(est), emp_se = sd(est), mean_se = mean(se), reps = .N),
+         by = .(design, contrast, outcome)]
+s <- merge(s, map, by = c("design", "contrast"), all.x = TRUE)
+s <- merge(s, tr[, .(outcome, estimand, truth = value)], by = c("outcome", "estimand"), all.x = TRUE)
+s[, `:=`(bias = mean_est - truth, mcse_bias = emp_se / sqrt(reps))]
+s[, z := bias / mcse_bias]
+print(s[order(design, outcome), .(design, contrast, outcome, truth = round(truth, 4), mean_est = round(mean_est, 4),
+          bias = round(bias, 4), z = round(z, 1), emp_se = round(emp_se, 4), mean_se = round(mean_se, 4))])
+fwrite(res, sprintf("results/validate_%s_%s.csv", world, scenario))