trial_msm: Fit the marginal structural model for the sequence of emulated trials

Description

Usage

trial_msm(
  data,
  outcome_cov = ~1,
  estimand_type = c("ITT", "PP", "As-Treated"),
  model_var = NULL,
  first_followup = NA,
  last_followup = NA,
  analysis_weights = c("asis", "unweighted", "p99", "weight_limits"),
  weight_limits = c(0, Inf),
  include_followup_time = ~followup_time + I(followup_time^2),
  include_trial_period = ~trial_period + I(trial_period^2),
  where_case = NA,
  glm_function = c("glm", "parglm"),
  use_sample_weights = TRUE,
  quiet = FALSE,
  ...
)

Value

Object of class TE_msm containing

model: a glm object
robust: a list containing a summary table of estimated regression coefficients and the robust covariance matrix
args: a list contain the parameters used to prepare and fit the model

Arguments

data

A data.frame containing all the required variables in the person-time format, i.e., the `long' format.

outcome_cov

A RHS formula with baseline covariates to be adjusted for in the marginal structural model for the emulated trials. Note that if a time-varying covariate is specified in outcome_cov, only its value at each of the trial baselines will be included in the expanded data.

estimand_type

Specify the estimand for the causal analyses in the sequence of emulated trials. estimand_type = "ITT" will perform intention-to-treat analyses, where treatment switching after trial baselines are ignored. estimand_type = "PP" will perform per-protocol analyses, where individuals' follow-ups are artificially censored and inverse probability of treatment weighting is applied. estimand_type = "As-Treated" will fit a standard marginal structural model for all possible treatment sequences, where individuals' follow-ups are not artificially censored but treatment switching after trial baselines are accounted for by applying inverse probability of treatment weighting.

model_var

Treatment variables to be included in the marginal structural model for the emulated trials. model_var = "assigned_treatment" will create a variable assigned_treatment that is the assigned treatment at the trial baseline, typically used for ITT and per-protocol analyses. model_var = "dose" will create a variable dose that is the cumulative number of treatments received since the trial baseline, typically used in as-treated analyses.

first_followup

First follow-up time/visit in the trials to be included in the marginal structural model for the outcome event.

last_followup

Last follow-up time/visit in the trials to be included in the marginal structural model for the outcome event.

analysis_weights

Choose which type of weights to be used for fitting the marginal structural model for the outcome event.

"asis": use the weights as calculated.
"p99": use weights truncated at the 1st and 99th percentiles (based on the distribution of weights in the entire sample).
"weight_limits": use weights truncated at the values specified in weight_limits.
"unweighted": set all analysis weights to 1, even if treatment weights or censoring weights were calculated.

weight_limits

Lower and upper limits to truncate weights, given as c(lower, upper)

include_followup_time

The model to include the follow up time/visit of the trial (followup_time) in the marginal structural model, specified as a RHS formula.

include_trial_period

The model to include the trial period (trial_period) in the marginal structural model, specified as a RHS formula.

where_case

Define conditions using variables specified in where_var when fitting a marginal structural model for a subgroup of the individuals. For example, if where_var= "age", where_case = "age >= 30" will only fit the marginal structural model to the subgroup of individuals. who are 30 years old or above.

glm_function

Specify which glm function to use for the marginal structural model from the stats or parglm packages. The default function is the glm function in the stats package. Users can also specify glm_function = "parglm" such that the parglm function in the parglm package can be used for fitting generalized linear models in parallel. The default control setting for parglm is nthreads = 4 and method = "FAST", where four cores and Fisher information are used for faster computation. Users can change the default control setting by passing the arguments nthreads and method in the parglm.control function of the parglm package, or alternatively, by passing a control argument with a list produced by parglm.control(nthreads = , method = ).

use_sample_weights

Use case-control sampling weights in addition to inverse probability weights for treatment and censoring. data must contain a column sample_weight. The final weights used in the pooled logistic regression are calculated as weight = weight * sample_weight.

quiet

Suppress the printing of progress messages and summaries of the fitted models.

...

Additional arguments passed to glm_function. This may be used to specify initial values of parameters or arguments to control. See stats::glm, parglm::parglm and parglm::parglm.control() for more information.

Details

Apply a weighted pooled logistic regression to fit the marginal structural model for the sequence of emulated trials and calculates the robust covariance matrix of parameter using the sandwich estimator.

The model formula is constructed by combining the arguments outcome_cov, model_var, include_followup_time, and include_trial_period.