Wu Ruiqian, Zhang Ying, Bakoyannis Giorgos
Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE.
Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN.
Stat Med. 2025 Feb 10;44(3-4):e10332. doi: 10.1002/sim.10332.
The semi-competing risks data model is a special type of disease-state model that focuses on studying the association between an intermediate event and a terminal event and proves to be a useful tool in modeling disease progression. The study of the semi-competing risk data model not only allows us to evaluate whether a disease episode is related to death but also provides a toolkit to predict death, given that the episode occurred at a certain time. However, the computation of the semi-competing risk models is a numerically challenging task. The Gamma-Frailty conditional Markov model has been shown to be an efficient computation model for studying semi-competing risks data. Building on recent advances in studying semi-competing risks data, this work proposes a non-parametric pseudo-likelihood method equipped with an EM-like algorithm to study semi-competing risks data with event misascertainment under the restricted Gamma-Frailty conditional Markov model. A thorough simulation study is conducted to demonstrate the inference validity of the proposed method and its numerical stability. The proposed method is applied to a large HIV cohort study, EA-IeDEA, that has a severe death under-reporting issue to assess the degree of adverse impact of the interruption of ART care on HIV mortality.
半竞争风险数据模型是一种特殊类型的疾病状态模型，专注于研究中间事件与终末事件之间的关联，并且已被证明是一种用于疾病进展建模的有用工具。对半竞争风险数据模型的研究不仅使我们能够评估疾病发作是否与死亡相关，而且还提供了一个工具包，用于在给定发作发生在特定时间的情况下预测死亡。然而，半竞争风险模型的计算是一项数值上具有挑战性的任务。伽马脆弱条件马尔可夫模型已被证明是研究半竞争风险数据的一种有效计算模型。基于对半竞争风险数据研究的最新进展，本文提出了一种配备类似期望最大化（EM）算法的非参数伪似然方法，用于在受限伽马脆弱条件马尔可夫模型下研究存在事件误判的半竞争风险数据。进行了全面的模拟研究，以证明所提出方法的推断有效性及其数值稳定性。所提出的方法应用于一项大型艾滋病队列研究EA - IeDEA，该研究存在严重的死亡漏报问题，以评估抗逆转录病毒治疗中断对艾滋病死亡率的不利影响程度。
