Crowther Michael J, Lambert Paul C
Department of Health Sciences, University of Leicester, Centre for Medicine, University Road, Leicester, LE1 7RH, UK.
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, S-171 77, Sweden.
Stat Med. 2017 Dec 20;36(29):4719-4742. doi: 10.1002/sim.7448. Epub 2017 Sep 5.
Multistate models are increasingly being used to model complex disease profiles. By modelling transitions between disease states, accounting for competing events at each transition, we can gain a much richer understanding of patient trajectories and how risk factors impact over the entire disease pathway. In this article, we concentrate on parametric multistate models, both Markov and semi-Markov, and develop a flexible framework where each transition can be specified by a variety of parametric models including exponential, Weibull, Gompertz, Royston-Parmar proportional hazards models or log-logistic, log-normal, generalised gamma accelerated failure time models, possibly sharing parameters across transitions. We also extend the framework to allow time-dependent effects. We then use an efficient and generalisable simulation method to calculate transition probabilities from any fitted multistate model, and show how it facilitates the simple calculation of clinically useful measures, such as expected length of stay in each state, and differences and ratios of proportion within each state as a function of time, for specific covariate patterns. We illustrate our methods using a dataset of patients with primary breast cancer. User-friendly Stata software is provided.
多状态模型越来越多地被用于对复杂的疾病概况进行建模。通过对疾病状态之间的转变进行建模，并考虑每次转变时的竞争事件，我们可以更深入地了解患者的病程以及风险因素在整个疾病路径中的影响方式。在本文中，我们专注于参数化多状态模型，包括马尔可夫模型和半马尔可夫模型，并开发了一个灵活的框架，其中每次转变都可以由多种参数模型指定，包括指数模型、威布尔模型、冈珀茨模型、罗伊斯顿 - 帕玛比例风险模型或对数逻辑斯蒂模型、对数正态模型、广义伽马加速失效时间模型，并且可能在不同转变之间共享参数。我们还扩展了该框架以允许时间相依效应。然后，我们使用一种高效且可推广的模拟方法来计算任何拟合多状态模型的转移概率，并展示它如何促进临床有用指标的简单计算，例如每个状态的预期停留时间，以及特定协变量模式下每个状态内比例随时间的差异和比率。我们使用原发性乳腺癌患者的数据集来说明我们的方法。并提供了用户友好的Stata软件。
