trajhrmsm_gform {trajmsm}

History Restricted MSM and Latent Class of Growth Analysis estimated with G-formula.

Description

Estimate parameters of LCGA-HRMSM using g-formula. and bootstrap to get standard errors.

Usage

trajhrmsm_gform(
  degree_traj = c("linear", "quadratic", "cubic"),
  rep = 50,
  treatment,
  covariates,
  baseline,
  outcome,
  ntimes_interval,
  total_followup,
  time,
  time_values,
  identifier,
  var_cov,
  number_traj = 3,
  family = "poisson",
  obsdata
)

Arguments

degree_traj

To specify the polynomial degree for modelling the time-varying treatment.

rep

Number of repetition for the bootstrap.

treatment

Name of the time-varying treatment.

covariates

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

baseline

Name of baseline covariates.

outcome

Name of the outcome variable.

ntimes_interval

Length of a time-interval (s).

total_followup

Total length of follow-up.

time

Name of the time variable.

time_values

Measuring times.

identifier

Name of the column of the unique identifier.

var_cov

Names of the time-varying covariates.

number_traj

Number of trajectory groups.

family

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

obsdata

Data in a long format.

Value

A list containing the following components:

results_hrmsm_gform

Matrix of estimates for LCGA-MSM, obtained using the g-formula method.

result_coef_boot

Matrix of estimates obtained with bootstrap.

restraj

Fitted trajectory model.

mean_adh

Matrix of mean adherence per trajectory group.

Author(s)

Awa Diop Denis Talbot

Examples

obsdata_long = gendata(n = 1000, format = "long", total_followup = 8,
timedep_outcome = TRUE,  seed = 945)
baseline_var <- c("age","sex")
years <- 2011:2018
variables <- c("hyper", "bmi")
covariates <- lapply(years, function(year) {
paste0(variables, year)})
treatment_var <- paste0("statins", 2011:2018)
var_cov <- c("statins","hyper", "bmi")
reshrmsm_gform = trajhrmsm_gform(degree_traj = "linear", rep=5 ,
treatment = treatment_var,covariates = covariates, baseline = baseline_var,
outcome = "y",var_cov = var_cov, ntimes_interval = 6, total_followup = 8,
 time = "time",time_values = years, identifier = "id",
number_traj = 3, family = "poisson", obsdata = obsdata_long)
reshrmsm_gform$results_hrmsm_gform