{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,9]],"date-time":"2026-11-09T18:40:36Z","timestamp":1794249636777,"version":"4.9.0"},"reference-count":44,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T00:00:00Z","timestamp":1778025600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100008637","name":"Center for Strategic Scientific Initiatives, National Cancer Institute","doi-asserted-by":"publisher","award":["U01-CA253913"],"award-info":[{"award-number":["U01-CA253913"]}],"id":[{"id":"10.13039\/100008637","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008637","name":"Center for Strategic Scientific Initiatives, National Cancer Institute","doi-asserted-by":"publisher","award":["U01-CA265750"],"award-info":[{"award-number":["U01-CA265750"]}],"id":[{"id":"10.13039\/100008637","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000026","name":"National Institute on Drug Abuse","doi-asserted-by":"publisher","award":["R37DA015612"],"award-info":[{"award-number":["R37DA015612"]}],"id":[{"id":"10.13039\/100000026","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Med Decis Making"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:p>Discrete event simulation (DES) is a flexible and computationally efficient approach for modeling diverse processes; however, DES remains underutilized in health care and medical decision making due to a lack of reliable and reproducible implementations. We developed an open-source DES framework to simulate individual-level state-transition models (iSTMs) in continuous time accounting for treatment effects, time dependence on state residence, and age-dependent mortality. Our DES implementation employs a modular and easily adaptable structure, with each module corresponding to a unique transition between health states. To simulate the evolution of the process (i.e., individual state transitions), we adapted the next-reaction algorithm from the stochastic chemical reactions literature. Simulation-time dependence (age-dependent mortality) and state residence time dependence (transition from sick to sicker) are seamlessly incorporated into the DES framework via validated nonparametric and parametric sampling routines (e.g., inversion method) of event times. Treatment effects are integrated as scaling factors of the hazard functions (proportional hazards). We illustrate the framework\u2019s benefits by implementing the Sick-Sicker Model in R and conduct a cost-effectiveness analysis and probabilistic analysis. We also obtain epidemiological outcomes of interest from the DES output, such as disease prevalence, survival probabilities, and distributions of state-specific dwell times. Our DES framework offers a reliable and accessible alternative that enables the simulation of more realistic dynamics of state-transition processes at potentially lower implementation and computational costs than discrete-time iSTMs.<\/jats:p>\n                  <jats:sec>\n                    <jats:title>Highlights<\/jats:title>\n                    <jats:list list-type=\"bullet\">\n                      <jats:list-item>\n                        <jats:p>Discrete event simulation (DES) is a flexible and efficient approach to simulate diverse processes in model-based decision analysis.<\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:p>The tutorial presents an open-source DES framework to simulate individual-level state-transition models (iSTMs) in continuous time.<\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:p>The modular structure of our DES framework accommodates treatment effects, time-dependent transitions, and age-dependent mortality using validated sampling methods.<\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:p>The coded example in R uses the Sick-Sicker Model to compute a cost-effectiveness analysis, epidemiological outcomes, probabilistic analysis, and value-of-information analysis.<\/jats:p>\n                      <\/jats:list-item>\n                    <\/jats:list>\n                  <\/jats:sec>","DOI":"10.1177\/0272989x261437826","type":"journal-article","created":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T07:00:19Z","timestamp":1778050819000},"page":"533-548","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["A Tutorial on Discrete Event Simulation Models Using a Cost-Effectiveness Analysis Example in 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