University of California Los Angeles, CA, USA.
Stat Methods Med Res. 2023 Aug;32(8):1511-1526. doi: 10.1177/09622802231176123. Epub 2023 Jul 14.
Multistate models are useful for studying exposures that affect transitions among a set of health states. However, they can be challenging to apply when exposures are time-varying. We develop a multistate model and a method of likelihood construction that allows application of the model to data in which interventions or other exposures can be time-varying and an individual may to be exposed to multiple intervention conditions while progressing through states. The model includes cure proportions, reflecting the possibility that some individuals will never leave certain states. We apply the approach to analyze patient vaccination data from a stepped wedge design trial evaluating two interventions to increase uptake of human papillomavirus vaccination. The states are defined as the number of vaccine doses the patient has received. We model state transitions as a semi-Markov process and include cure proportions to account for individuals who will never leave a given state (e.g. never receive their next dose). Multistate models typically quantify intervention effects as hazard ratios contrasting the intensities of transitions between states in intervention versus control conditions. For multistate processes, another clinically meaningful outcome is the change in the percentage of the study population that has achieved a specific state (e.g. completion of all required doses) by a specific point in time due to an intervention. We present a method for quantifying intervention effects in this manner. We apply the model to both simulated and real-world data and also explore some conditions under which such models may give biased results.
多状态模型对于研究影响一组健康状态之间转变的暴露因素非常有用。然而，当暴露因素随时间变化时，应用这些模型可能会具有挑战性。我们开发了一种多状态模型和一种似然构造方法，该方法允许将模型应用于干预或其他暴露因素随时间变化的情况下，并且个体在通过状态时可能会接触到多种干预条件的数据中。该模型包括治愈率，反映了一些个体永远不会离开某些状态的可能性。我们将该方法应用于分析一项阶梯式楔形设计试验的患者疫苗接种数据，该试验评估了两种干预措施以提高人乳头瘤病毒疫苗接种率。状态定义为患者接受的疫苗剂量数。我们将状态转移建模为半马尔可夫过程，并包括治愈率，以说明永远不会离开特定状态的个体（例如，永远不会接受下一次剂量）。多状态模型通常将干预效果量化为危险比，对比干预和对照条件下状态之间转移的强度。对于多状态过程，另一个具有临床意义的结果是由于干预，在特定时间点达到特定状态（例如完成所有要求的剂量）的研究人群百分比的变化。我们提出了一种以这种方式量化干预效果的方法。我们将模型应用于模拟和真实世界的数据，并探索了这些模型可能产生偏差结果的一些情况。
