trajhrmsm_ipw {trajmsm}

History Restricted MSM and Latent Class of Growth Analysis estimated with IPW.

Description

Estimate parameters of LCGA-HRMSM using IPW.

Usage

trajhrmsm_ipw(
  degree_traj = c("linear", "quadratic", "cubic"),
  numerator = c("stabilized", "unstabilized"),
  identifier,
  baseline,
  covariates,
  treatment,
  outcome,
  var_cov,
  include_censor = FALSE,
  ntimes_interval,
  total_followup,
  time,
  time_values,
  family = "poisson",
  censor = censor,
  number_traj,
  obsdata,
  weights = NULL,
  treshold = 0.999
)

Arguments

degree_traj

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

numerator

To choose between stabilized and unstabilized weights.

identifier

Name of the column of the unique identifier.

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.

var_cov

Names of the time-varying covariates.

include_censor

Logical, if TRUE, includes censoring.

ntimes_interval

Length of a time-interval (s).

total_followup

Total length of follow-up.

time

Name of the time variable.

time_values

Values of the time variable.

family

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

censor

Name of the censoring variable.

number_traj

Number of trajectory groups.

obsdata

Data in a long format.

weights

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

treshold

For weight truncation.

Value

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

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","y")
reshrmsm_ipw <- trajhrmsm_ipw(degree_traj = "linear", numerator = "stabilized",
identifier = "id", baseline = baseline_var,
covariates = covariates, treatment = treatment_var,
outcome = "y", var_cov= var_cov,include_censor = FALSE,
 ntimes_interval = 6,total_followup = 8, time = "time", time_values = 2011:2018,
family = "poisson", number_traj = 3, obsdata = obsdata_long, treshold = 0.999)
reshrmsm_ipw$res_trajhrmsm_ipw