Updates 12

DESCRIPTION

@@ -1,22 +1,27 @@
 Package: trajmsm
 Type: Package
 Title: Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories
-Version: 0.1.0
-Author: Awa Diop, Denis Talbot
+Version: 0.1.2
+Authors@R: c(
+    person("Awa", "Diop", email = "awa.diop.2@ulaval.ca", role = c("aut", "cre")),
+    person("Denis", "Talbot", role = "aut"))
 Maintainer: Awa Diop <awa.diop.2@ulaval.ca>
-Description: The package trajmsm is based on the paper Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories:https://journals.sagepub.com/doi/pdf/10.1177/09622802231202384. 
-License: GPL-3
+Description: Implements marginal structural models combined with latent class growth analysis framework for assessing the causal effect of treatment trajectories. Based on the approach described in "Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories" (https://journals.sagepub.com/doi/pdf/10.1177/09622802231202384).
+License: GPL (>= 3)
 Encoding: UTF-8
 Imports: 
     class,
     e1071,
     flexmix,
     geepack,
     ggplot2,
-    survival
+    survival,
+    sandwich
 LazyData: true
 Suggests: 
     knitr,
     rmarkdown
 VignetteBuilder: knitr
 RoxygenNote: 7.2.0
+URL: https://github.com/awamaeva/R-package-trajmsm
+BugReports: https://github.com/awamaeva/R-package-trajmsm/issues 

NAMESPACE

@@ -1,21 +1,24 @@
 # Generated by roxygen2: do not edit by hand
 
-export(IPCW_sw)
-export(IPCW_w)
-export(IPTW_sw)
-export(IPTW_w)
-export(IPW)
-export(buildtraj)
-export(gendatatraj)
-export(genobsdata_msm)
-export(ggplotraj)
-export(longtowide)
-export(trajMSM_IPW)
-export(trajMSM_gform)
-export(trajMSM_pooled_ltmle)
-export(widetolong)
+export(build_traj)
+export(gendata)
+export(gformula)
+export(ggtraj)
+export(inverse_probability_weighting)
+export(pltmle)
+export(predict_traj)
+export(split_data)
+export(stabilized_ipcw)
+export(stabilized_iptw)
+export(trajhrmsm_gform)
+export(trajhrmsm_ipw)
+export(trajmsm_gform)
+export(trajmsm_ipw)
+export(trajmsm_pltmle)
+export(unstabilized_ipcw)
+export(unstabilized_iptw)
 import(e1071)
 import(flexmix)
 import(ggplot2)
-importFrom(geepack,geeglm)
+import(sandwich)
 importFrom(survival,coxph)

R/IPW.R

@@ -14,13 +14,14 @@
 #' @export
 #' @author Awa Diop, Denis Talbot
 #' @examples
-#' Obsdata = gendata(n = 1000, format = "wide",total_followup = 3, seed = 945)
+#' obsdata = gendata(n = 1000, format = "wide",total_followup = 3, seed = 945)
 #' baseline_var <- c("age","sex")
-#' covariates <- list(c("hyper2011", "bmi2011"),c("hyper2012", "bmi2012"),c("hyper2013", "bmi2013"))
+#' covariates <- list(c("hyper2011", "bmi2011"),
+#' c("hyper2012", "bmi2012"),c("hyper2013", "bmi2013"))
 #' treatment_var <- c("statins2011","statins2012","statins2013")
-#' stabilized_weights = inverse_probability_weighting(numerator = "stabilized", identifier = "id",
-#'         covariates = covar, treatment = treatment_var, baseline = baseline_var,
-#'           total_followup = 3,obsdata = Obsdata)
+#' stabilized_weights = inverse_probability_weighting(numerator = "stabilized",
+#' identifier = "id", covariates = covariates, treatment = treatment_var,
+#' baseline = baseline_var, total_followup = 3,obsdata = obsdata)
 
 inverse_probability_weighting <- function(numerator = c("stabilized", "unstabilized"), identifier,
                                           baseline, covariates, treatment,

R/build_traj.R

@@ -13,9 +13,9 @@
 #' @import flexmix
 #' @export build_traj
 #' @examples
-#' obsdata_long = gendata_trajmsm(n = 1000,format = "long", seed = 345)
+#' obsdata_long = gendata(n = 1000,format = "long", total_followup = 6, seed = 945)
 #' formula = as.formula(cbind(statins, 1 - statins) ~ time)
-#' restraj = build_traj(obsdata = obsdata_long, number_traj = 3, formula = Formula, identifier = "id")
+#' restraj = build_traj(obsdata = obsdata_long, number_traj = 3, formula = formula, identifier = "id")
 
 build_traj <- function(obsdata, formula, number_traj, identifier, family = "binomial", seed = 945,
                        control = list(iter.max = 1000, minprior = 0), ...) {

R/gendata.R

@@ -7,7 +7,7 @@
 #' @export gendata
 #' @return A data frame with generated trajectories.
 #' @examples
-#' gendata(n = 100, include_censor = FALSE, format = "wide",total_followup = 3)
+#' gendata(n = 100, include_censor = FALSE, format = "wide",total_followup = 3, seed = 945)
 
 gendata<- function(n, include_censor = FALSE, format = c("long", "wide"),start_year = 2011, total_followup, timedep_outcome = FALSE, seed) {
   set.seed(seed)
@@ -21,13 +21,13 @@ gendata<- function(n, include_censor = FALSE, format = c("long", "wide"),start_y
   statins <- hyper <- bmi <- censor <- matrix(NA, nrow = n, ncol = total_followup)
   bmi[, 1] <-   rbinom(n, 1, plogis(0.15 * age + 0.7 * sex))
   hyper[, 1] <-  rbinom(n, 1, plogis(.15 * age + 0.7 * sex + 0.1 * bmi[, 1]))
-  statins[, 1] <- rbinom(n, 1, plogis(-.15 + 0.4 * age + 0.25* sex - 0.1 * bmi[, 1] - 0.2*hyper[, 1]))
+  statins[, 1] <- rbinom(n, 1, plogis(.5 + 0.4 * age + 0.25* sex - 0.1 * bmi[, 1] - 0.2*hyper[, 1]))
   censor[, 1] <-  rbinom(n, 1, plogis(-2 + 0.2 * age + 0.01 * sex + 0.1 * bmi[, 1] - 0.2*hyper[, 1] - 0.5*statins[, 1]))
   # Generate data based on conditions
   for (i in 2:total_followup) {
     bmi[, i] <- rbinom(n, 1, plogis(0.15 * age + 0.7 * sex - 0.25 * statins[, i-1]))
     hyper[, i] <- rbinom(n, 1, plogis(0.15 * age + 0.7 * sex + 0.1 * bmi[, i] - 0.35 * statins[, i-1]))
-    statins[, i] <- rbinom(n, 1, plogis(-0.15 + 0.1 * age + 0.1 * sex - 0.1 * bmi[, i] - 0.2*hyper[, i] + (seq(0.15,1,length.out = total_followup)[i-1])* statins[, i-1] ))
+    statins[, i] <- rbinom(n, 1, plogis(.5 + 0.1 * age + 0.1 * sex - 0.1 * bmi[, i] - 0.2*hyper[, i] + (seq(0.15,1,length.out = total_followup)[i-1])* statins[, i-1] ))
     if (include_censor) {
       censor[, i] <- rbinom(n, 1, plogis(-2 + 0.02 * age + 0.01 * sex - 0.5 * statins[, i] + 0.1 * hyper[, i] + 0.2 * bmi[, i]))
     }
@@ -124,5 +124,3 @@ gendata<- function(n, include_censor = FALSE, format = c("long", "wide"),start_y
 return(obsdata)
 }
 
-obsdata <- gendata(n = 100, format = "long",timedep_outcome = FALSE, total_followup = 8, seed = 945)
-obsdata <- gendata(n = 100, format = "long",timedep_outcome = TRUE, total_followup = 8, seed = 945)

R/gformula.R

@@ -10,16 +10,9 @@
 #' @param obsdata observed data in wide format.
 #' @returns  \item{list_gform_countermeans}{Counterfactual means obtained with g-formula.}
 #' @import e1071
+#' @export
 #' @author Awa Diop, Denis Talbot
-#' @examples
-#' obsdata_wide = gendata_trajmsm(n = 1000, format = "wide", seed = 945)
-#' baseline_var <- c("age","sex")
-#' covariates <- list(c("hyper2011", "bmi2011"),c("hyper2012", "bmi2012"),c("hyper2013", "bmi2013"))
-#' treatment_var <- c("statins2011","statins2012","statins2013")
-#' res_gform = gformula(formula = as.formula(" y ~ statins2011 + statins2012 + statins2013 + hyper2011 + bmi2011 + hyper2012 + bmi2012 +
-#'                                               hyper2013 + bmi2013 + age + sex " ), baseline = baseline, covariates = covariates,
-#' treatment = treatment, outcome = outcome, ntimes_interval = 3, obsdata = obsdata_wide)
-#' res_gform$counter_means
+
 
 gformula <- function(formula, baseline, covariates, treatment, outcome, ntimes_interval, obsdata) {
 nregimes <- 2^ntimes_interval  # Number of treatment regimes

R/ggtraj.R

@@ -14,7 +14,8 @@
 #' @export ggtraj
 #' @examples
 #' obsdata_long = gendata(n = 1000, format = "long", total_followup = 12, seed = 945)
-#' restraj = build_traj(obsdata = obsdata_long, number_traj = 3, formula = as.formula(cbind(statins, 1 - statins) ~ time), identifier = "id")
+#' restraj = build_traj(obsdata = obsdata_long, number_traj = 3,
+#' formula = as.formula(cbind(statins, 1 - statins) ~ time), identifier = "id")
 #' datapost = restraj$data_post
 #' head(datapost)
 #' traj_data_long <- merge(obsdata_long, datapost, by = "id")

R/pltmle.R

@@ -1,6 +1,6 @@
 #' @title Counterfactual means for a Pooled LTMLE
 #' @description function to estimate counterfactual means for a pooled LTMLE.
-#' @name sub_pltmle
+#' @name pltmle
 #' @param formula specification of the model for the outcome to be fitted.
 #' @param identifier  name of the column for unique identifiant.
 #' @param covariates covariates.
@@ -13,32 +13,37 @@
 #' @returns  \item{list_pltmle_countermeans}{Counterfactual means and influence functions with the pooled ltmle.}
 #' \item{D}{Influence functions}
 #' @import e1071
+#' @export
 #' @examples
-#' Obsdata_long = gendata_trajmsm(n = 2000, format = "long", seed = 945)
+#' obsdata_long = gendata(n = 2000, format = "long",total_followup = 3, seed = 945)
 #' baseline_var <- c("age","sex")
-#' covariates <- list(c("hyper2011", "bmi2011"),c("hyper2012", "bmi2012"),c("hyper2013", "bmi2013"))
+#' covariates <- list(c("hyper2011", "bmi2011"),
+#' c("hyper2012", "bmi2012"),c("hyper2013", "bmi2013"))
 #' treatment_var <- c("statins2011","statins2012","statins2013")
 #' time_values <- c(2011,2012,2013)
 #' formulaA = as.formula(cbind(statins, 1 - statins) ~ time)
-#' restraj = build_traj(obsdata = Obsdata_long, number_traj = 3, formula = formulaA, identifier = "id")
+#' restraj = build_traj(obsdata = obsdata_long, number_traj = 3,
+#' formula = formulaA, identifier = "id")
 #' datapost = restraj$data_post
-#' trajmsm_long <- merge(Obsdata_long, datapost, by = "id")
+#' trajmsm_long <- merge(obsdata_long, datapost, by = "id")
 #'     AggFormula <- as.formula(paste("statins", "~", "time", "+", "class"))
 #'     AggTrajData <- aggregate(AggFormula, data = trajmsm_long, FUN = mean)
 #'     AggTrajData
 #' trajmsm_long[ , "traj_group"] <- trajmsm_long[ , "class"]
 #' trajmsm_wide = reshape(trajmsm_long, direction = "wide", idvar = "id",
 #' v.names = c("statins","bmi","hyper"), timevar = "time", sep ="")
-#' formulaY =  as.formula(" y ~ statins2011 + statins2012 + statins2013 + hyper2011 + bmi2011 + hyper2012 + bmi2012 +
-#'                                     hyper2013 + bmi2013 + age + sex ")
+#' formula =  as.formula(" y ~ statins2011 + statins2012 + statins2013 +
+#' hyper2011 + bmi2011 + hyper2012 + bmi2012 +
+#'  hyper2013 + bmi2013 + age + sex ")
 #' class = factor(predict_traj(identifier = "id", total_followup = 3,
 #'         treatment = "statins", time = "time", time_values = time_values,
 #'         trajmodel = restraj$traj_model)$post_class);
-#' traj_indic=t(sapply(1:nregimes,function(x)sapply(1:number_traj,function(i) ifelse(class[x]==i,1,0))))
-#' traj_indic[,1]=1
-#' res_pltmle = pltmle(formula = formulaY, outcome = outcome,treatment = treatment_var,
-#'                   covariates = covar, baseline = baseline_var, ntimes_interval = 3, number_traj = 3,
-#'                  time =  "Time",time_values = time_values,identifier = "id",obsdata = trajmsm_wide,traj=traj_indic, treshold = 0.99)
+#' traj=t(sapply(1:8,function(x)sapply(1:3,function(i)ifelse(class[x]==i,1,0))))
+#' traj[,1]=1
+#' res_pltmle = pltmle(formula = formula, outcome = "y",treatment = treatment_var,
+#' covariates = covariates, baseline = baseline_var, ntimes_interval = 3, number_traj = 3,
+#'  time =  "time",time_values = time_values,identifier = "id",obsdata = trajmsm_wide,
+#' traj=traj, treshold = 0.99)
 #' res_pltmle$counter_means
 #' @author Awa Diop, Denis Talbot
 
@@ -83,7 +88,7 @@ pltmle <- function(formula, outcome, treatment, covariates, baseline, ntimes_int
   Weights = inverse_probability_weighting(identifier = identifier, covariates = covariates,
                                           treatment = treatment, baseline = baseline,
                                           total_followup = total_followup, numerator = "unstabilized",
-                                          include_censor = include_censor, censor = censor,obsdata = obsdata)[[1]];
+                                          include_censor = FALSE, censor = censor,obsdata = obsdata)[[1]];
 
   weights_trunc <- sapply(1:ntimes_interval, function(x){
   weights <- ifelse(quantile(Weights[, x], treshold, na.rm = TRUE)> Weights[, x], quantile(Weights[, x], treshold, na.rm = TRUE), Weights[, x])