Marginal Structural Illness-Death Models for Semi-Competing Risks Data

TL;DR

This paper introduces marginal structural illness-death models for semi-competing risks data, enabling causal inference in observational studies with complex event structures, and demonstrates their application and implementation in R.

Contribution

It develops new marginal structural models for illness-death data, including a frailty-based approach, with inference methods and practical R software implementation.

Findings

Validated models through extensive simulations.

Applied models to study alcohol exposure and cognitive impairment.

Provided publicly available R package for implementation.

Abstract

The three state illness death model has been established as a general approach for regression analysis of semi competing risks data. For observational data the marginal structural models (MSM) are a useful tool, under the potential outcomes framework to define and estimate parameters with causal interpretations. In this paper we introduce a class of marginal structural illness death models for the analysis of observational semi competing risks data. We consider two specific such models, the Markov illness death MSM and the frailty based Markov illness death MSM. For interpretation purposes, risk contrasts under the MSMs are defined. Inference under the illness death MSM can be carried out using estimating equations with inverse probability weighting, while inference under the frailty based illness death MSM requires a weighted EM algorithm. We study the inference procedures under both MSMs using extensive simulations, and apply them to the analysis of mid life alcohol exposure on late life cognitive impairment as well as mortality using the Honolulu Asia Aging Study data set. The R codes developed in this work have been implemented in the R package semicmprskcoxmsm that is publicly available on CRAN.