Marginal Structural Modeling of Representative Treatment Trajectories

TL;DR

This paper introduces a novel method for summarizing complex treatment histories in marginal structural models using latent growth curve analysis, improving causal interpretation in observational studies.

Contribution

It develops a new approach to parameterize outcome models by identifying representative treatment trajectories through latent class analysis, enhancing MSM interpretability.

Findings

Effective summarization of treatment trajectories in MSMs.

Application to lung transplant data elucidates Tacrolimus effects.

Improved causal inference with complex treatment patterns.

Abstract

Marginal structural models (MSMs) are widely used in observational studies to estimate the causal effect of time-varying treatments. Despite its popularity, limited attention has been paid to summarizing the treatment history in the outcome model, which proves particularly challenging when individuals' treatment trajectories exhibit complex patterns over time. Commonly used metrics such as the average treatment level fail to adequately capture the treatment history, hindering causal interpretation. For scenarios where treatment histories exhibit distinct temporal patterns, we develop a new approach to parameterize the outcome model. We apply latent growth curve analysis to identify representative treatment trajectories from the observed data and use the posterior probability of latent class membership to summarize the different treatment trajectories. We demonstrate its use in parameterizing the MSMs, which facilitates the interpretations of the results. We apply the method to analyze data from an existing cohort of lung transplant recipients to estimate the effect of Tacrolimus concentrations on the risk of incident chronic kidney disease.