trajmsm_ipw {trajmsm}

Marginal Structural Model and Latent Class of Growth Analysis estimated with IPW

Description

Estimate parameters of LCGA-MSM using IPW.

Usage

trajmsm_ipw(
  formula1,
  formula2,
  family,
  identifier,
  treatment,
  covariates,
  baseline,
  obsdata,
  numerator = "stabilized",
  include_censor = FALSE,
  censor,
  weights = NULL,
  treshold = 0.99
)

Arguments

formula1

Specification of the model for the outcome to be fitted for a binomial or gaussian distribution.

formula2

Specification of the model for the outcome to be fitted for a survival outcome.

family

Specification of the error distribution and link function to be used in the model.

identifier

Name of the column of the unique identifier.

treatment

Time-varying treatment.

covariates

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

baseline

Name of the baseline covariates.

obsdata

Dataset to be used in the analysis.

numerator

Type of weighting ("stabilized" or "unstabilized").

include_censor

Logical, if TRUE, includes censoring.

censor

Name of the censoring variable.

weights

A vector of estimated weights. If NULL, the weights are computed by the function IPW.

treshold

For weight truncation.

Value

Provides a matrix of estimates for LCGA-MSM, obtained using IPW.

Provides a matrix of estimates for LCGA-MSM, obtained using IPW.

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$data_post
trajmsm_long <- merge(obsdata_long, datapost, by = "id")
    AggFormula <- as.formula(paste("statins", "~", "time", "+", "class"))
    AggTrajData <- aggregate(AggFormula, data = trajmsm_long, FUN = mean)
    AggTrajData
trajmsm_long$ipw_group <- relevel(trajmsm_long$class, ref = "1")
obsdata = 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_ipw = trajmsm_ipw(formula1 = as.formula("y ~ ipw_group"),
           identifier = "id", baseline = baseline_var, covariates = covariates,
           treatment = treatment_var, family = "binomial",
           obsdata = obsdata,numerator = "stabilized", include_censor = FALSE, treshold = 0.99)
resmsm_ipw