submission

CausalInference · Dec 2, 2024 · e9deff9 · e9deff9
1 parent 36d00b9
commit e9deff9
Show file tree

Hide file tree

Showing 12 changed files with 178 additions and 229 deletions.
diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
@@ -0,0 +1,3 @@
+Version: 0.1.0
+Date: 2024-12-01 17:39:28 UTC
+SHA: 36d00b91829dc9bb06f4b60996041137a30b8aa9
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -18,7 +18,7 @@ Authors@R: c(person("Zhaoxi", "Cheng", role = c("aut", "cre"),
                       email = "mhernan@hsph.harvard.edu"),
               person("2024 The President and Fellows of Harvard College", 
                       role = c("cph")))
-Description: Implements iterative conditional expectation (ICE) estimators of the plug-in g-formula. 
+Description: Implements iterative conditional expectation (ICE) estimators of the plug-in g-formula (Wen, Young, Robins, and Hernán (2020) <doi: 10.1111/biom.13321>). 
     Both singly robust and doubly robust ICE estimators based on parametric models are available. 
     The package can be used to estimate survival curves under sustained treatment strategies (interventions) using longitudinal data with time-varying treatments, time-varying confounders, censoring, and competing events.
     The interventions can be static or dynamic, and deterministic or stochastic (including threshold interventions). Both prespecified and user-defined interventions are available.

diff --git a/NEWS.md b/NEWS.md
@@ -0,0 +1,3 @@
+# gfoRmulaICE 0.1.0
+
+* Initial CRAN submission.
diff --git a/R/ICE.R b/R/ICE.R
diff --git a/R/ICE_pool.R b/R/ICE_pool.R
@@ -87,15 +87,15 @@
 #' ice_static <- ice_pool(data = data, K = 5, 
 #' id = "id", time_name = "t0", outcome_name = "Y",
 #' censor_name = "C", competing_name = "D",
-#' total_effect = F, 
+#' total_effect = FALSE, 
 #' outcome_model = Y ~ L1 + L2 + A1 + A2, 
 #' censor_model = C ~ L1 + L2 + A1 + A2,
 #' competing_model = D ~ L1 + L2 + A1 + A2, 
 #' hazard_model = Y ~ L1 + L2 + A1 + A2,
 #' interventions = list(static(1)),
 #' intervention_names = list("A"),
-#' hazard_based = T, 
-#' global_hazard = F,
+#' hazard_based = TRUE, 
+#' global_hazard = FALSE,
 #' intervention_description = "Always Treat")
 #' 
 #' ice_static
@@ -107,12 +107,12 @@
 #' ice_natural_course <- ice_pool(data = data, K = 5, 
 #' id = "id", time_name = "t0", outcome_name = "Y",
 #' censor_name = "C", competing_name = "D",
-#' total_effect = T, 
+#' total_effect = TRUE, 
 #' outcome_model = Y ~ L1 + L2 + A1 + A2, 
 #' censor_model = C ~ L1 + L2 + A1 + A2,
 #' interventions = list(natural_course()),
 #' intervention_names = list("A"),
-#' hazard_based = F, 
+#' hazard_based = FALSE, 
 #' intervention_description = "Natural Course")
 #'
 #' ice_natural_course
@@ -126,12 +126,12 @@
 #' ice_dynamic <- ice_pool(data = data, K = 5, 
 #' id = "id", time_name = "t0", outcome_name = "Y",
 #' censor_name = "C", competing_name = "D",
-#' total_effect = F, 
+#' total_effect = FALSE, 
 #' outcome_model = Y ~ L1 + L2 + A1 + A2, 
 #' censor_model = C ~ L1 + L2 + A1 + A2,
-#' interventions = list(dynamic("L1 > 0", static(0), static(1), absorb = T)),
+#' interventions = list(dynamic("L1 > 0", static(0), static(1), absorb = TRUE)),
 #' intervention_names = list("A"),
-#' hazard_based = F, 
+#' hazard_based = FALSE, 
 #' intervention_description = "Dynamic Treat")
 #'
 #' ice_dynamic
@@ -143,14 +143,14 @@
 #' ice_grace_period <- ice_pool(data = data, K = 5, 
 #' id = "id", time_name = "t0", outcome_name = "Y",
 #' censor_name = "C", competing_name = "D",
-#' total_effect = T, 
+#' total_effect = TRUE, 
 #' outcome_model = Y ~ L1 + L2 + A1 + A2, 
 #' censor_model = C ~ L1 + L2 + A1 + A2,
 #' competing_model = D ~ L1 + L2 + A1 + A2,
 #' hazard_model = Y ~ L1 + L2 + A1 + A2,
 #' interventions = list(grace_period("uniform", 2, "L1 = 0")),
 #' intervention_names = list("A"),
-#' hazard_based = T,
+#' hazard_based = TRUE,
 #' intervention_description = "Dynamic Treat Grace Period")
 #'
 #' ice_grace_period
@@ -163,14 +163,14 @@
 #' ice_threshold <- ice_pool(data = data, K = 5, 
 #' id = "id", time_name = "t0", outcome_name = "Y",
 #' censor_name = "C", competing_name = "D",
-#' total_effect = T, 
+#' total_effect = TRUE, 
 #' outcome_model = Y ~ L1 + L2 + A1 + A2, 
 #' censor_model = C ~ L1 + L2 + A1 + A2,
 #' competing_model = D ~ L1 + L2 + A1 + A2,
 #' hazard_model = Y ~ L1 + L2 + A1 + A2, 
 #' interventions = list(threshold(-3, Inf)),
 #' intervention_names = list("A"),
-#' hazard_based = T,
+#' hazard_based = TRUE,
 #' intervention_description = "Threshold Intervention")
 #'
 #' ice_threshold
@@ -181,7 +181,7 @@ ice_pool <- function(data, K, id, time_name, outcome_name,
                      competing_model = NULL, hazard_model = NULL, 
                      interventions,
                      intervention_names, intervention_times = NULL,
-                     compute_nc_risk = T, hazard_based, global_hazard = NULL,
+                     compute_nc_risk = TRUE, hazard_based, global_hazard = NULL,
                      intervention_description, verbose = TRUE)
                      # global_haz_model, )
 {
@@ -194,8 +194,8 @@ ice_pool <- function(data, K, id, time_name, outcome_name,
   intervention_varnames <- intervention_names
   time0 <- time_name
 
-  if (hazard_based == F) {
-    global_hazard <- F
+  if (hazard_based == FALSE) {
+    global_hazard <- FALSE
   }
 
   if (is.null(intervention_times) | (length(intervention_times[[1]]) == 0)) {
@@ -427,7 +427,7 @@ ice_pool <- function(data, K, id, time_name, outcome_name,
         competing_covar_nc <- competing_covar
       }
 
-      if (total_effect == F) {
+      if (total_effect == FALSE) {
 
         competing_formula <- as.formula(paste0(competing_varname, "~",
                                                paste0(competing_covar_nc, collapse = "+")))
@@ -477,7 +477,7 @@ ice_pool <- function(data, K, id, time_name, outcome_name,
   abar_all <- list()
   data <- as.data.frame(data)
 
-  assign.global(F, "gp_indicator")
+  assign.global(FALSE, "gp_indicator")
 
   assign.global(data, "interv_data")
 
@@ -572,7 +572,7 @@ ice_pool <- function(data, K, id, time_name, outcome_name,
 
   comp_init <- c()
 
-  if (!is.null(competing_varname) & (total_effect == T) & hazard_based) {
+  if (!is.null(competing_varname) & (total_effect == TRUE) & hazard_based) {
     formula_full_comp <- as.formula(paste0(competing_varname,"~", paste0(c(competing_covar), collapse = "+")))
     yfitog_comp = speedglm(formula_full_comp, family = binomial(), data = data)
     paramcomp = (yfitog_comp)$coef
@@ -723,14 +723,14 @@ ice_pool <- function(data, K, id, time_name, outcome_name,
   ## 5. regression at each time point
 
   if (verbose) {
-  cat("Running ", intervention_description[[1]], "... \n")
+  message("Running ", intervention_description[[1]], "... \n")
   }
     for (i in 1:K) {
 
       t <- K - i + 1
 
       if (verbose) {
-      cat(paste0("Running Time ", t, "...", "\n"))
+      message(paste0("Running Time ", t, "...", "\n"))
       }
 
       covar_t <- paste0(outcome_covar, sep = paste0("_", t - 1))
@@ -739,7 +739,7 @@ ice_pool <- function(data, K, id, time_name, outcome_name,
         if (t == 1) {
           data_pred_tmp <- data_fit <- tmpdata
         } else {
-          if (!is.null(competing_varname) & total_effect == F) {
+          if (!is.null(competing_varname) & total_effect == FALSE) {
           data_pred_tmp <- data_fit <- tmpdata[!is.na(tmpdata[, paste0(censor_varname, "_", t - 1)]) & tmpdata[, paste0(censor_varname, "_", t - 1)] == 0 &
                                                  !is.na(tmpdata[, paste0(competing_varname, "_", t - 2)]) & tmpdata[, paste0(competing_varname, "_", t - 2)] == 0, ]
           } else {
@@ -819,7 +819,7 @@ ice_pool <- function(data, K, id, time_name, outcome_name,
               data_fit = tmpdata
             } else {
 
-              if (!is.null(competing_varname) & total_effect == F) {
+              if (!is.null(competing_varname) & total_effect == FALSE) {
               data_fit = tmpdata[!is.na(tmpdata[, paste0(censor_varname, "_", iter - 1)]) & tmpdata[, paste0(censor_varname, "_", iter - 1)] == 0 &
                                    !is.na(tmpdata[, paste0(competing_varname, "_", iter - 2)]) & tmpdata[, paste0(competing_varname, "_", iter - 2)] == 0, ]
               } else {
@@ -868,7 +868,7 @@ ice_pool <- function(data, K, id, time_name, outcome_name,
                   has_time <- str_detect(haz_global_covar, time_name)
 
                   if (any(has_time)) {
-                    time_idx <- which(has_time == T)
+                    time_idx <- which(has_time == TRUE)
                     haz_global_covar_tmp <- haz_global_covar[-time_idx]
                     pred_data <- data.frame(matrix(NA, 
                                                    ncol = length(haz_global_covar_tmp) + length(time_idx), 
@@ -894,7 +894,7 @@ ice_pool <- function(data, K, id, time_name, outcome_name,
                 }
 
               ## need to calculate d_hat and multiply it with the hazard
-              if (!is.null(competing_varname) & total_effect == T) {
+              if (!is.null(competing_varname) & total_effect == TRUE) {
                 predict_comp <- plogis(as.matrix(covar_mat)  %*% matrix(paramcomp, nrow = length(paramcomp)))
                 data_fit[, paste0("y", t, "pred")] <- predict(fit_temp, newdata = data_fit, type = "response") * (1 - predict_tmp) * (1 - predict_comp) + predict_tmp
               } else {
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,3 @@
		# gfoRmulaICE 0.1.0

		* Initial CRAN submission.