Semicompeting risks data are a mixture of competing risks data and progressive state data. This type of data occurs when a nonterminal event is subject to truncation by a well‐defined terminal event, but not vice versa. The shared gamma‐frailty conditional Markov model (GFCMM) has been used to analyze semicompeting risks data because of its flexibility. There are two versions of this model: the restricted and the unrestricted model. Maximum likelihood estimation methodology has been proposed in the literature. However, we found through numerical experiments that the unrestricted model sometimes yields nonparametrically biased estimation. In this article, we provide a practical guideline for using the GFCMM in the analysis of semicompeting risk data that includes: (a) a score test to assess if the restricted model, which does not exhibit estimation problems, is reasonable under a proportional hazards assumption, and (b) a graphical illustration to justify whether the unrestricted model yields nonparametric estimation with substantial bias for cases where the test provides a statistical significant result against the restricted model. This guideline was applied to the Indianapolis‐Ibadan Dementia Project data as an illustration to explore how dementia occurrence changes mortality risk.

中文翻译：

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关于半竞争风险数据的共享伽玛-脆弱条件马尔可夫模型。

半竞争风险数据是竞争风险数据和进步状态数据的混合。当非终止事件因定义良好的终止事件而被截断，而不是相反时，将发生这种类型的数据。共享伽玛脆弱条件马尔可夫模型（GFCMM）由于其灵活性而被用于分析半竞争风险数据。此模型有两个版本：受限制的模型和不受限制的模型。在文献中已经提出了最大似然估计方法。但是，我们通过数值实验发现，无限制模型有时会产生非参数偏差估计。在本文中，我们提供了使用GFCMM进行半竞争风险数据分析的实用指南，其中包括：（a）得分测试，以评估受限模型，在比例风险假设下，它不存在估计问题，并且是合理的；以及（b）图形说明，以证明在测试提供了针对受限模型的统计显着性结果的情况下，非受限模型是否会产生带有重大偏差的非参数估算。该指南已应用于印第安纳波利斯-伊巴丹痴呆症项目数据，以举例说明痴呆症的发生如何改变死亡风险。