diff --git a/pvisim/R/estimators.R b/pvisim/R/estimators.R index a7c829b..b108bb8 100644 --- a/pvisim/R/estimators.R +++ b/pvisim/R/estimators.R @@ -21,13 +21,15 @@ VARIANTS <- c("V1_ignore", "V2_cluster", "V3_random", "V4_within") #' MALE and no PVI before the decision in month t: the risk set of the PVI model. pvi_risk_set <- function(rec) { p <- rec$patients - pm <- merge(rec$pm, p[, c("id", "phys_id", BASE_COVS, "t_pvi", "pvi_elective", "t_male", "t_death"), + extra <- intersect("t_pvi_orig", names(p)) + pm <- merge(rec$pm, p[, c("id", "phys_id", BASE_COVS, "t_pvi", "pvi_elective", "t_male", "t_death", extra), with = FALSE], by = "id") pm <- pm[(is.na(t_death) | t_death > t) & (is.na(t_male) | t_male > t) & (is.na(t_pvi) | t_pvi >= t)] pm[, pvi := as.integer(!is.na(t_pvi) & t_pvi == t)] sev0 <- rec$pm[t == 0, .(id, sev0 = sev_obs)] pm <- merge(pm, sev0, by = "id") pm[, `:=`(win = as.integer(t < rec$par$size$window), tt = pmin(t, 12))] + if (length(extra)) pm[, pvi_before := as.integer(!is.na(t_pvi_orig) & t_pvi_orig < t)] pm[] } @@ -41,7 +43,7 @@ pvi_risk_set <- function(rec) { #' variant: V1/V2 no physician terms; V3 physician random intercept (glmer, nAGQ = 0); #' V4 physician fixed effects (fixest). Under V4 a physician with no PVI in a phase has #' no estimable intercept for it; the maximum-likelihood probability there is 0. -fit_pvi <- function(data, newdata, variant) { +fit_pvi <- function(data, newdata, variant, extra_covs = NULL) { out <- numeric(nrow(newdata)) for (w in c(1L, 0L)) { i_new <- newdata$win == w @@ -49,13 +51,14 @@ fit_pvi <- function(data, newdata, variant) { d <- data[win == w] nd <- newdata[i_new] tt_levels <- if (w == 1L) "factor(tt)" else "splines::ns(t, df = 4)" - out[i_new] <- fit_pvi_phase(d, nd, variant, tt_levels) + # no exposure at all in a phase (the NCE after the window): probability 0 + out[i_new] <- if (sum(d$pvi) == 0) 0 else fit_pvi_phase(d, nd, variant, tt_levels, extra_covs) } out } -fit_pvi_phase <- function(d, nd, variant, time_terms) { - covs <- c(BASE_COVS, "sev_obs", "sev0") +fit_pvi_phase <- function(d, nd, variant, time_terms, extra_covs = NULL) { + covs <- c(BASE_COVS, "sev_obs", "sev0", extra_covs) if (variant == "V4_within") covs <- setdiff(covs, "obl") rhs <- paste(c(covs, time_terms), collapse = " + ") if (variant %in% c("V1_ignore", "V2_cluster")) { @@ -161,12 +164,13 @@ run_design <- function(rec, design, variant, outcomes = c(male = "t_male", disea nco1 = "t_nco1", nco2 = "t_nco2")) { rec <- restrict_variant(rec, variant) rs <- if (design %in% c("ccw", "seq")) pvi_risk_set(rec) else NULL - phat <- if (design == "ccw") fit_pvi(rs, rs, variant) else NULL + phat <- if (design == "ccw") fit_pvi(rs, rs, variant, rec$extra_covs) else NULL + 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)), - ccw = est_ccw(rec, outcomes[[o]], variant, rs = copy(rs), phat = phat), + 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" @@ -178,13 +182,13 @@ run_design <- function(rec, design, variant, outcomes = c(male = "t_male", disea #' Naive "ever early PVI" design: exposure = PVI in months 0..W-1, follow-up from month 0, #' baseline propensity score. Immortal time is present by construction. -est_naive <- function(rec, outcome = "t_male", variant = "V1_ignore", horizon = 60L) { +est_naive <- function(rec, outcome = "t_male", variant = "V1_ignore", horizon = 60L, extra_covs = NULL) { W <- rec$par$size$window p <- copy(rec$patients) p[, `:=`(t_out = get(outcome), arm = ifelse(!is.na(t_pvi) & t_pvi < W, "early", "none"))] p <- merge(p, rec$pm[t == 0, .(id, sev_obs)], by = "id") p[, pvi := as.integer(arm == "early")] - ps <- fit_pvi_ps(p, variant, covs = c(BASE_COVS, "sev_obs")) # sev_obs is the month-0 value here + ps <- fit_pvi_ps(p, variant, covs = c(BASE_COVS, "sev_obs", extra_covs)) # sev_obs is the month-0 value here p[, w := ifelse(arm == "early", 1 / ps, 1 / (1 - ps))] d <- expand_followup(p[!is.na(w)], 0L, horizon) rd_from_aj(aj_cif(d, horizon), "early", "none") @@ -212,7 +216,7 @@ fit_pvi_ps <- function(p, variant, covs = c(BASE_COVS, "sev_obs", "sev0")) { #' Landmark at month W (D13): patients alive and MALE-free at the start of month W; #' exposure = PVI in months 0..W-1; covariates at diagnosis and at month W-1; #' outcome counted in months W..horizon-1. -est_landmark <- function(rec, outcome = "t_male", variant = "V1_ignore", horizon = 60L) { +est_landmark <- function(rec, outcome = "t_male", variant = "V1_ignore", horizon = 60L, extra_covs = NULL) { W <- rec$par$size$window p <- copy(rec$patients)[(is.na(t_death) | t_death >= W) & (is.na(t_male) | t_male >= W)] p[, `:=`(t_out = get(outcome), arm = ifelse(!is.na(t_pvi) & t_pvi < W, "early", "none"))] @@ -220,7 +224,7 @@ est_landmark <- function(rec, outcome = "t_male", variant = "V1_ignore", horizon p <- merge(p, rec$pm[t == W - 1L, .(id, sev_obs)], by = "id") p <- merge(p, rec$pm[t == 0, .(id, sev0 = sev_obs)], by = "id") p[, pvi := as.integer(arm == "early")] - ps <- fit_pvi_ps(p, variant) + ps <- fit_pvi_ps(p, variant, covs = c(BASE_COVS, "sev_obs", "sev0", extra_covs)) p[, w := ifelse(arm == "early", 1 / ps, 1 / (1 - ps))] d <- expand_followup(p[!is.na(w)], W, horizon) rd_from_aj(aj_cif(d, horizon), "early", "none") @@ -300,3 +304,18 @@ est_seq <- function(rec, outcome = "t_male", variant = "V1_ignore", horizon = 60 data.table(est = sum(res$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_) } + +#' Negative-control exposure (D32, D36): the early low-value procedure (NCE, months +#' 0..W-1) takes the place of early PVI as the exposure; early-PVI status is adjusted +#' for (time-varying "PVI before t" in CCW, "PVI in the window" at the landmark). +#' Only the landmark and CCW (A vs B1) designs are run. +nce_records <- function(rec, adjust_pvi = TRUE) { + W <- rec$par$size$window + p <- copy(rec$patients) + p[, `:=`(t_pvi_orig = t_pvi, pvi_before = as.integer(!is.na(t_pvi) & t_pvi < W))] + p[, `:=`(t_pvi = t_nce, pvi_elective = !is.na(t_nce))] + rec$patients <- p + rec$extra_covs <- if (adjust_pvi) "pvi_before" else NULL + rec$comparators <- "B1" + rec +} diff --git a/pvisim/R/limits.R b/pvisim/R/limits.R new file mode 100644 index 0000000..b7eff78 --- /dev/null +++ b/pvisim/R/limits.R @@ -0,0 +1,29 @@ +# Own estimands of the non-interventional designs (plan 5.5). The landmark and naive +# contrasts have no intervention that defines them; their own estimand is what the same +# estimator gives with every confounder measured (oracle): exact severity, true frailty +# and smoking, and the latent physician traits. A large-n limit is too noisy (the +# landmark estimator has few treated patients), so the oracle estimator is run on every +# replicate's own data: estimation bias = mean(estimate - oracle estimate), paired, and +# the oracle mean estimates the own estimand. + +#' The replicate's records with the hidden truth in place of the claims proxies. +oracle_records <- function(rec) { + o <- rec + o$pm <- merge(rec$pm[, .(id, t)], rec$hidden_pm, by = c("id", "t"))[, .(id, t, sev_obs = z)] + h <- rec$hidden + o$patients <- merge(copy(rec$patients)[, c("frailty_index", "smoking") := NULL], + h[, .(id, frailty_index = frailty, smoking = smoking_true, skill, style)], by = "id") + o$extra_covs <- c("skill", "style") + o +} + +#' Oracle versions of the landmark and naive designs (V1, patient-level SE). +run_oracle <- function(rec, outcomes = c(male = "t_male", disease = "t_disease", nco1 = "t_nco1", nco2 = "t_nco2")) { + o <- oracle_records(rec) + rbindlist(lapply(names(outcomes), function(nm) { + lm <- est_landmark(o, outcomes[[nm]], "V1_ignore", extra_covs = o$extra_covs) + nv <- est_naive(o, outcomes[[nm]], "V1_ignore", extra_covs = o$extra_covs) + rbind(cbind(design = "landmark", lm), cbind(design = "naive", nv))[ + , `:=`(outcome = nm, contrast = "early_none", variant = "oracle", se = se_patient)] + })) +} diff --git a/pvisim/R/records.R b/pvisim/R/records.R index 305db8d..c7834f2 100644 --- a/pvisim/R/records.R +++ b/pvisim/R/records.R @@ -43,7 +43,8 @@ make_records <- function(pop, course, par) { hidden <- data.table(id = pat$id, skill = pat$skill, style = pat$style, frailty = pat$frailty, smoking_true = pat$smoking_true, rate = pat$rate, t_clti_true = ev$t_clti_true) - list(patients = patients, pm = pm, hidden = hidden, par = par) + hidden_pm <- data.table(id = id, t = t, z = course$z[idx]) + list(patients = patients, pm = pm, hidden = hidden, hidden_pm = hidden_pm, par = par) } #' Stop if an analysis table carries a latent column. diff --git a/simulations/03_limits.R b/simulations/03_limits.R new file mode 100644 index 0000000..0337bbd --- /dev/null +++ b/simulations/03_limits.R @@ -0,0 +1,20 @@ +# RCT ITT truths for every world x scenario (the landmark and naive own estimands are +# estimated within replicates by run_oracle()). Writes simulations/truths/truths_rct.csv. +# Usage: Rscript simulations/03_limits.R [n] [cores] +pkgload::load_all("pvisim", quiet = TRUE) +suppressPackageStartupMessages(library(data.table)) +a <- commandArgs(TRUE) +n <- if (length(a) >= 1) as.numeric(a[1]) else 5e5 +cores <- if (length(a) >= 2) as.integer(a[2]) else 6L +raw <- yaml::read_yaml(default_params_path()) +jobs <- CJ(world = names(raw$worlds), scenario = names(raw$scenarios), sorted = FALSE) +res <- parallel::mclapply(seq_len(nrow(jobs)), function(i) { + w <- jobs$world[i]; s <- jobs$scenario[i] + par <- load_params(w, s) + rbind(rct_truth(par, "realistic", n = 1e6)[outcome %in% c("male", "disease", "nco1", "nco2")], + rct_truth(par, "ideal", n = 1e6)[outcome %in% c("male", "disease", "nco1", "nco2")]) +}, mc.cores = cores) +bad <- vapply(res, inherits, TRUE, "try-error") +if (any(bad)) stop(res[bad][[1]]) +fwrite(rbindlist(res), "simulations/truths/truths_rct.csv") +cat("wrote simulations/truths/truths_rct.csv\n") diff --git a/simulations/config/experiments.yml b/simulations/config/experiments.yml new file mode 100644 index 0000000..cd4b497 --- /dev/null +++ b/simulations/config/experiments.yml @@ -0,0 +1,12 @@ +# Experiment grids (plan sections 5.2, 7, 9). Run with simulations/run_experiment.R [--scale draft|final] +main: + worlds: [W0, W1, W1_rho0, W2, W3, W4, W5, WR] + scenarios: ["null", harm, benefit_harm] + designs: [naive, landmark, ccw, seq] + variants: [V1_ignore, V3_random, V4_within] # V1 also yields V2 (cluster-robust SE) + nce: true # negative-control exposure (landmark, CCW), D32/D36 + rct: [realistic, ideal] +scales: + smoke: {n_patients: 20000, reps: 1} + draft: {n_patients: 50000, reps: 100} + final: {n_patients: 50000, reps: 500} # to be revisited after the draft (sample size, D25/D29) diff --git a/simulations/run_experiment.R b/simulations/run_experiment.R new file mode 100644 index 0000000..4030dd7 --- /dev/null +++ b/simulations/run_experiment.R @@ -0,0 +1,60 @@ +# Run an experiment grid; one CSV per world x scenario x replicate, so runs are resumable. +# Usage: Rscript simulations/run_experiment.R main [--scale draft] [--cores 10] [--worlds W0,W2] +pkgload::load_all("pvisim", quiet = TRUE) +suppressPackageStartupMessages(library(data.table)) +a <- commandArgs(TRUE) +opt <- function(flag, default) { i <- match(flag, a); if (is.na(i)) default else a[i + 1] } +grid_name <- a[1] +scale <- opt("--scale", "draft") +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) +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)] +cat(sprintf("%s/%s: %d jobs, %d to do, %d cores\n", grid_name, scale, nrow(jobs), nrow(todo), cores)) + +one_job <- function(i) { + j <- todo[i] + par <- load_params(j$world, j$scenario) + rec <- simulate_records(par, rep = j$rep, n = sc$n_patients) + out <- list() + for (v in g$variants) { + for (d in g$designs) { + r <- run_design(rec, d, v) + if (v == "V1_ignore") { # same fit, two SEs + r2 <- copy(r)[, `:=`(variant = "V2_cluster", se = se_cluster)] + r <- rbind(r, r2) + } + out[[length(out) + 1]] <- r[, analysis := "main"] + } + if (isTRUE(g$nce)) for (d in c("landmark", "ccw")) { + r <- run_design(nce_records(rec), d, v, outcomes = c(male = "t_male", disease = "t_disease")) + if (v == "V1_ignore") r <- rbind(r, copy(r)[, `:=`(variant = "V2_cluster", se = se_cluster)]) + out[[length(out) + 1]] <- r[, analysis := "nce"] + } + } + for (d in c("landmark", "ccw")) { # unadjusted NCE (supplement, D36), V1/V2 only + r <- run_design(nce_records(rec, adjust_pvi = FALSE), d, "V1_ignore", outcomes = c(male = "t_male", disease = "t_disease")) + out[[length(out) + 1]] <- r[, analysis := "nce_unadjusted"] + } + out[[length(out) + 1]] <- run_oracle(rec)[, analysis := "main"] # own estimand of landmark / naive + for (dz in g$rct) { + r <- run_rct(par, dz, rep = j$rep) + out[[length(out) + 1]] <- r[, `:=`(contrast = analysis, analysis = "rct", variant = "none", se = se_patient)] + } + res <- rbindlist(out, fill = TRUE)[, `:=`(world = j$world, scenario = j$scenario, rep = j$rep)] + dir.create(dirname(j$file), recursive = TRUE, showWarnings = FALSE) + fwrite(res, j$file) + NULL +} +t0 <- Sys.time() +res <- parallel::mclapply(seq_len(nrow(todo)), function(i) tryCatch(one_job(i), error = function(e) + sprintf("%s/%s/%d: %s", todo$world[i], todo$scenario[i], todo$rep[i], conditionMessage(e))), + mc.cores = cores, mc.preschedule = FALSE) +err <- unlist(res[!vapply(res, is.null, TRUE)]) +cat(sprintf("done in %.1f h; %d errors\n", as.numeric(Sys.time() - t0, units = "hours"), length(err))) +dir.create(outdir, recursive = TRUE, showWarnings = FALSE) +if (length(err)) { writeLines(err, file.path(outdir, sprintf("errors_%s.txt", format(Sys.time(), "%Y%m%d_%H%M")))); cat(head(err, 3), sep = "\n") } diff --git 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+WR,benefit_harm,death,seq_itt_m5,0.0245593317660327,0.000229208511522821,923030 +WR,benefit_harm,nco1,seq_itt_m5,0.000595863623067299,5.06265285316315e-05,923030 +WR,benefit_harm,nco2,seq_itt_m5,0.000114839170991583,3.62569334918711e-05,923030 diff --git a/simulations/validate_designs.R b/simulations/validate_designs.R index 82b880b..0dbd6dc 100644 --- a/simulations/validate_designs.R +++ b/simulations/validate_designs.R @@ -19,7 +19,8 @@ res <- rbindlist(parallel::mclapply(seq_len(reps), function(r) { fill = TRUE) }, mc.cores = cores), fill = TRUE) -tr <- fread("simulations/truths/truths.csv")[world == ..world & scenario == ..scenario] +w_ <- world; s_ <- scenario +tr <- fread("simulations/truths/truths.csv")[world == w_ & scenario == s_] 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),