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

Yiran Zhang, Andrew Ying, Steve Edland, Lon White and Ronghui Xu ()
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Yiran Zhang: School of Public Health and Human Longevity Science
Andrew Ying: Google Inc.
Steve Edland: School of Public Health and Human Longevity Science
Lon White: Pacific Health Research and Education Institute
Ronghui Xu: School of Public Health and Human Longevity Science

Statistics in Biosciences, 2024, vol. 16, issue 3, No 7, 668-692

Abstract: 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.

Keywords: Cox model; Frailty; IPW; Multi-state model; Potential outcomes; Risk contrasts; Transition intensity (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12561-023-09413-6

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