Cohort State-Transition Models in R: A Tutorial.

Decision models can synthesize evidence from different sources to simulate long-term consequences of different strategies in the presence of uncertainty. Cohort state-transition models (cSTM) are decision models commonly used in medical decision making to simulate hypothetical cohorts' transitions across various health states over time. This tutorial shows how to implement cSTMs in R, an open-source mathematical and statistical programming language. As an example, we use a previously published cSTM-based cost-effectiveness analysis. With this example, we illustrate both time-independent cSTMs, where transition probabilities are constant over time, and time-dependent cSTMs, where transition probabilities vary by age and are dependent on time spent in a health state (state residence). We also illustrate how to compute various epidemiological outcomes of interest, such as survival and prevalence. We demonstrate how to calculate economic outcomes and conducting a cost-effectiveness analysis of multiple strategies using the example model, and provide additional resources to conduct probabilistic sensitivity analyses. We provide a link to a public repository with all the R code described in this tutorial that can be used to replicate the example or be adapted for various decision modeling applications.