trajmsm: An R package for Trajectory Analysis and Causal Modeling

TL;DR

The trajmsm R package simplifies the estimation of models combining trajectory analysis with causal modeling, enabling consistent parameter estimation using multiple estimators for complex longitudinal data.

Contribution

It introduces an R package that integrates latent class growth analysis with marginal structural models, facilitating causal inference in trajectory-based studies.

Findings

Supports inverse probability weighting, g-computation, and pooled LTMLE estimators.

Provides functions for LCGA-MSM and LCGA-HRMSM models.

Available on CRAN since version 0.1.3.

Abstract

The R package trajmsm provides functions designed to simplify the estimation of the parameters of a model combining latent class growth analysis (LCGA), a trajectory analysis technique, and marginal structural models (MSMs) called LCGA-MSM. LCGA summarizes similar patterns of change over time into a few distinct categories called trajectory groups, which are then included as "treatments" in the MSM. MSMs are a class of causal models that correctly handle treatment-confounder feedback. The parameters of LCGA-MSMs can be consistently estimated using different estimators, such as inverse probability weighting (IPW), g-computation, and pooled longitudinal targeted maximum likelihood estimation (pooled LTMLE). These three estimators of the parameters of LCGA-MSMs are currently implemented in our package. In the context of a time-dependent outcome, we previously proposed a combination of LCGA and history-restricted MSMs (LCGA-HRMSMs). Our package provides additional functions to estimate the parameters of such models. Version 0.1.3 of the package is currently available on CRAN.