Two Cox-based multistate modeling approaches are compared for analyzing a complex multicohort event history process. The first approach incorporates cohort information as a fixed covariate, thereby providing a direct estimation of the cohort-specific effects. The second approach includes the cohort as stratum variable, thus giving an extra flexibility in estimating the transition probabilities. Additionally, both approaches may include possible interaction terms between the cohort and a given prognostic predictor. Furthermore, the Markov property conditional on observed prognostic covariates is assessed using a global score test. Whenever departures from the Markovian assumption are revealed for a given transition, the time of entry into the current state is incorporated as a fixed covariate, yielding a semi-Markov process. The two proposed methods are applied to a three-wave dataset of COVID-19-hospitalized adults in the southern Barcelona metropolitan area (Spain), and the corresponding performance is discussed. While both semi-Markovian approaches are shown to be useful, the preferred one will depend on the focus of the inference. To summarize, the cohort-covariate approach enables an insightful discussion on the the behavior of the cohort effects, whereas the stratum-cohort approach provides flexibility to estimate transition-specific underlying risks according with the different cohorts

翻译：比较了两种基于Cox的多状态建模方法，用于分析复杂的多队列事件历史过程。第一种方法将队列信息作为固定协变量纳入模型，从而直接估计队列特定效应。第二种方法将队列作为分层变量，在估计转移概率时提供额外灵活性。此外，两种方法均可包含队列与给定预后预测变量之间的交互项。进一步地，利用全局评分检验评估基于观测预后协变量的马尔可夫性质。当某一转移过程偏离马尔可夫假设时，将进入当前状态的时间作为固定协变量纳入，从而形成半马尔可夫过程。这两种方法被应用于西班牙南巴塞罗那都会区新冠住院成年人的三波数据集，并讨论了相应性能。结果表明，两种半马尔可夫方法均有效，但选择哪种方法取决于推断重点。总结而言，队列-协变量方法能够深入揭示队列效应的行为特征，而分层-队列方法则提供了根据不同队列估计转移特定潜在风险的灵活性。