chendun1995andClaude Opus 5.5
fc1461d5b5
- 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]>
18 lines
1.1 KiB
R
18 lines
1.1 KiB
R
# Explore effect sizes: RD(A - B1) by month for MALE and the disease composite in W0.
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pkgload::load_all("pvisim", quiet = TRUE)
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suppressMessages(library(data.table))
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args <- commandArgs(TRUE)
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grid <- if (length(args)) eval(parse(text = args[1])) else
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list(c(0, 0), c(0.3, 0.03), c(1.2, 0.04))
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par0 <- load_params("W0", "null")
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pop <- make_population(par0, n = 2e5, seed = 9L)
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ci <- function(t, h) mean(!is.na(t) & t < h)
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for (g in grid) {
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par <- load_params("W0", "null", overrides = list(effect = list(relief = g[1], kappa = g[2])))
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ev <- lapply(c("A", "B1"), function(p) simulate_course(pop, par, policy(p), seed = 10L, record = FALSE)$events)
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rd <- function(col, h) 100 * (ci(ev[[1]][[col]], h) - ci(ev[[2]][[col]], h))
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cat(sprintf("relief %.2f kappa %.3f | disease RD @12 %+.1f @24 %+.1f @60 %+.1f | MALE RD @60 %+.1f (A %.1f%% B1 %.1f%%) | death RD %+.2f\n",
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g[1], g[2], rd("t_disease", 12), rd("t_disease", 24), rd("t_disease", 60), rd("t_male", 60),
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100 * ci(ev[[1]]$t_male, 60), 100 * ci(ev[[2]]$t_male, 60), rd("t_death", 60)))
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}
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