  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.
