Cohort statetransition models in R: From conceptualization to implementation
Abstract
Decision models can synthesize evidence from different sources to provide estimates of longterm consequences of a decision with uncertainty. Cohort statetransition models (cSTM) are decision models commonly used in medical decision making because they can simulate hypothetical cohorts' transitions across various health states over time. This tutorial shows how to conceptualize cSTMs in a programming language environment and shows examples of their implementation in R. We illustrate their use in a costeffectiveness analysis of a treatment using a previously published testbed cSTM. Both timeindependent cSTM where transition probabilities are constant over time and timedependent cSTM where transition probabilities vary over time are represented. For the timedependent cSTM, we consider transition probabilities dependent on age and state residence. We also illustrate how this setup can facilitate the computation of epidemiological outcomes of interest, such as survival and prevalence. We conclude by demonstrating how to calculate economic outcomes and conducting a costeffectiveness analysis of a treatment compared to usual care using the testbed model. 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 to be modified to suit different decision modeling needs.
 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 31 pages and 11 figures. For R code, see https://github.com/DARTHgit/Cohortmodelingtutorial