trajmsm: An R package for Trajectory Analysis and Causal Modeling
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.
Cite
@article{arxiv.2410.19682,
title = {trajmsm: An R package for Trajectory Analysis and Causal Modeling},
author = {Awa Diop and Caroline Sirois and Jason R. Guertin and Mireille E. Schnitzer and James M. Brophy and Denis Talbot},
journal= {arXiv preprint arXiv:2410.19682},
year = {2024}
}
Comments
19 pages, 13 tables, 3 figures