Cheon Kyeongmi, Thoma Marie E, Kong Xiangrong, Albert Paul S
Biometrics Research, Merck, West Point, PA 19486, U.S.A.
Stat Med. 2014 Aug 15;33(18):3204-13. doi: 10.1002/sim.6151. Epub 2014 Mar 27.
Markov models used to analyze transition patterns in discrete longitudinal data are based on the limiting assumption that individuals follow the common underlying transition process. However, when one is interested in diseases with different disease or severity subtypes, explicitly modeling subpopulation-specific transition patterns may be appropriate. We propose a model which captures heterogeneity in the transition process through a finite mixture model formulation and provides a framework for identifying subpopulations at different risks. We apply the procedure to longitudinal bacterial vaginosis study data and demonstrate that the model fits the data well. Further, we show that under the mixture model formulation, we can make the important distinction between how covariates affect transition patterns unique to each of the subpopulations and how they affect which subgroup a participant will belong to. Practically, covariate effects on subpopulation-specific transition behavior and those on subpopulation membership can be interpreted as effects on short-term and long-term transition behavior. We further investigate models with higher-order subpopulation-specific transition dependence.
用于分析离散纵向数据中转变模式的马尔可夫模型基于个体遵循共同潜在转变过程这一极限假设。然而，当人们对具有不同疾病或严重程度亚型的疾病感兴趣时，明确地对特定亚群体的转变模式进行建模可能是合适的。我们提出了一个模型，该模型通过有限混合模型公式捕捉转变过程中的异质性，并提供了一个识别不同风险亚群体的框架。我们将该方法应用于纵向细菌性阴道病研究数据，并证明该模型与数据拟合良好。此外，我们表明，在混合模型公式下，我们可以在协变量如何影响每个亚群体特有的转变模式以及它们如何影响参与者将属于哪个亚组之间做出重要区分。实际上，协变量对特定亚群体转变行为的影响以及对亚群体成员身份的影响可以解释为对短期和长期转变行为的影响。我们进一步研究了具有高阶特定亚群体转变依赖性的模型。
