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

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

pvisim/R/truths.R

@@ -9,7 +9,7 @@
 # Landmark, naive and RCT estimands are not interventional in this sense; they are added
 # with the estimators (oracle large-sample limits).
 
-OUTCOMES <- c(male = "t_male", disease = "t_disease", clti = "t_clti_code", amp = "t_amp",
+OUTCOMES <- c(limb = "t_limb", male = "t_male", disease = "t_disease", clti = "t_clti_code", amp = "t_amp",
               bypass = "t_bypass", rr = "t_rr", death = "t_death", nco1 = "t_nco1", nco2 = "t_nco2")
 
 truth_population <- function(par, n, seed) {
@@ -83,7 +83,7 @@ compute_truths <- function(world, scenario, n = 1e6, seed = 20261027L, horizon =
                                        value = rd, mcse = sqrt(var / n_elig), n = n_elig)]
   }
 
-  curves <- rbindlist(lapply(names(ev), function(p) rbindlist(lapply(c("male", "disease"), function(o)
+  curves <- rbindlist(lapply(names(ev), function(p) rbindlist(lapply(c("limb", "male", "disease"), function(o)
     data.table(world = world, scenario = scenario, policy = p, outcome = o, month = seq_len(horizon),
                cuminc = vapply(seq_len(horizon), function(h) mean(by_h(ev[[p]][[OUTCOMES[[o]]]], h)), 0))))))
   if (verbose) message(sprintf("%s/%s: %.1f min", world, scenario,