trajmsm_pltmle {trajmsm}

Pooled LTMLE

Description

Estimate parameters of LCGA-MSM using pooled LTMLE with influence functions to estimate standard errors.

Usage

trajmsm_pltmle(
  formula = formula,
  identifier,
  baseline,
  covariates,
  treatment,
  outcome,
  number_traj,
  total_followup,
  time,
  time_values,
  trajmodel,
  ref,
  obsdata,
  treshold = 0.999
)

Arguments

formula

Specification of the model for the outcome to be fitted.

identifier

Name of the column for unique identifiant.

baseline

Names of the baseline covariates.

covariates

Names of the time-varying covariates (should be a list).

treatment

Name of the time-varying treatment.

outcome

Name of the outcome variable.

number_traj

An integer to choose the number of trajectory groups.

total_followup

Total length of follow-up.

time

Name of the time variable.

time_values

Measuring times.

trajmodel

Trajectory model built with the observed treatment.

ref

The reference group.

obsdata

Observed data in wide format.

treshold

For weight truncation.

Value

Provides a matrix of estimates for LCGA-MSM, obtained using the pooled ltlmle method.

results_msm_pooledltmle

Estimates of a LCGA-MSM with pooled LTMLE.

Author(s)

Awa Diop, Denis Talbot

Examples

obsdata_long = gendata(n = 1000, format = "long", total_followup = 6, seed = 945)
years <- 2011:2016
baseline_var <- c("age","sex")
variables <- c("hyper", "bmi")
covariates <- lapply(years, function(year) {
paste0(variables, year)})
treatment_var <- paste0("statins", 2011:2016)
formula_treatment = as.formula(cbind(statins, 1 - statins) ~ time)
restraj = build_traj(obsdata = obsdata_long, number_traj = 3,
formula = formula_treatment, identifier = "id")
datapost = restraj$data_post
trajmsm_long <- merge(obsdata_long, datapost, by = "id")
    AggFormula <- as.formula(paste("statins", "~", "time", "+", "class"))
    AggTrajData <- aggregate(AggFormula, data = trajmsm_long, FUN = mean)
    AggTrajData
trajmsm_wide = reshape(data = trajmsm_long, direction = "wide", idvar = "id",
v.names = c("statins","bmi","hyper"), timevar = "time", sep ="")
formula = paste0("y ~", paste0(treatment_var,collapse = "+"), "+",
                paste0(unlist(covariates), collapse = "+"),"+",
                paste0(baseline_var, collapse = "+"))
resmsm_pltmle <- trajmsm_pltmle(formula = formula, identifier = "id", baseline = baseline_var,
 covariates = covariates, treatment = treatment_var, outcome = "y",
 time = "time", time_values = years, number_traj = 3, total_followup = 6,
 trajmodel = restraj$traj_model, ref = "1", obsdata = trajmsm_wide, treshold = 0.99)
 resmsm_pltmle