An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example.
An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example.
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DOI：
10.1177/0272989x221103163
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发表时间：
2023-01
影响因子：
3.6
通讯作者：
Jalal, Hawre
中科院分区：
文献类型：
--
作者：
Alarid-Escudero, Fernando;Krijkamp, Eline;Enns, Eva A.;Yang, Alan;Hunink, M. G. Myriam;Pechlivanoglou, Petros;Jalal, Hawre
决策模型可以将不同来源的信息联合收割机结合起来，模拟在不确定性存在的情况下，替代战略的长期后果。队列状态转换模型（cSTM）是一种常用于医疗决策的决策模型，用于模拟假设队列随时间在各种健康状态之间的转换。本教程重点介绍与时间无关的cSTM，其中健康状态之间的转移概率随时间保持不变。我们在R中实现了与时间无关的cSTM，R是一种开源的数学和统计编程语言。我们说明了时间无关的cSTM使用以前发表的决策模型，计算成本和有效性的结果，进行成本效益分析的多个策略，包括概率敏感性分析。我们在R中提供开源代码，以促进更广泛的采用。在第二个更高级的教程中，我们说明了时间依赖的cSTM。
Decision models can combine information from different sources to simulate the long-term consequences of alternative strategies in the presence of uncertainty. A cohort state-transition model (cSTM) is a decision model commonly used in medical decision-making to simulate the transitions of a hypothetical cohort among various health states over time. This tutorial focuses on time-independent cSTM, where transition probabilities among health states remain constant over time. We implement time-independent cSTM in R, an open-source mathematical and statistical programming language. We illustrate time-independent cSTMs using a previously published decision model, calculate costs and effectiveness outcomes, conduct a cost-effectiveness analysis of multiple strategies, including a probabilistic sensitivity analysis. We provide open-source code in R to facilitate wider adoption. In a second, more advanced tutorial, we illustrate time-dependent cSTMs.