Cohort StateTransition Models in R: A Tutorial
Abstract
Decision models can synthesize evidence from different sources to simulate longterm consequences of different strategies in the presence of uncertainty. Cohort statetransition 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 opensource mathematical and statistical programming language. As an example, we use a previously published cSTMbased costeffectiveness analysis. With this example, we illustrate both timeindependent cSTMs, where transition probabilities are constant over time, and timedependent 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 costeffectiveness 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.
 Publication:

arXiv eprints
 Pub Date:
 January 2020
 arXiv:
 arXiv:2001.07824
 Bibcode:
 2020arXiv200107824A
 Keywords:

 Statistics  Applications;
 Quantitative Biology  Quantitative Methods;
 Statistics  Computation;
 97M60 (Primary) 92D30 92B15;
 60J10 (Secondary);
 G.3;
 I.6.5;
 J.3;
 J.4
 EPrint:
 Tutorial with 48 pages and 12 figures. For R code, see https://github.com/DARTHgit/Cohortmodelingtutorial