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

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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.

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Acknowledgements

This research was partially supported by NIH/NIA grant R03 AG062432.

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Authors and Affiliations

  1. Biostatistics and Bioinformatics, School of Public Health and Human Longevity Science, San Diego, USA

    Yiran Zhang, Steve Edland & Ronghui Xu

  2. Department of Mathematics and Halicioglu Data Science Institute, University of California, San Diego, San Diego, USA

    Ronghui Xu

  3. Google Inc., Los Angeles, USA

    Andrew Ying

  4. Pacific Health Research and Education Institute, Honolulu, HI, USA

    Lon White

Authors
  1. Yiran Zhang
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  2. Andrew Ying
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  3. Steve Edland
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  4. Lon White
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  5. Ronghui Xu
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Corresponding author

Correspondence to Ronghui Xu.

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Zhang, Y., Ying, A., Edland, S. et al. Marginal Structural Illness-Death Models for Semi-competing Risks Data. Stat Biosci (2024). https://doi.org/10.1007/s12561-023-09413-6

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