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.

翻译：三状态疾病-死亡模型已被确立为半竞争风险数据回归分析的通用方法。对于观测数据，边际结构模型（MSM）在潜在结果框架下成为定义和估计具有因果解释参数的有效工具。本文提出一类用于分析观测性半竞争风险数据的边际结构疾病-死亡模型。我们考虑两种具体模型：马尔可夫疾病-死亡MSM和基于脆弱性的马尔可夫疾病-死亡MSM。为便于解释，定义了MSM下的风险对比指标。疾病-死亡MSM下的推断可通过带逆概率加权的估计方程实现，而基于脆弱性的疾病-死亡MSM下的推断需要采用加权EM算法。我们通过大量模拟研究了两种MSM下的推断流程，并将其应用于火奴鲁鲁亚洲老龄化研究数据集，分析中年酒精暴露对晚年认知障碍及死亡率的影响。本工作开发的R代码已集成至CRAN公开可用R包semicmprskcoxmsm中。