Changing Time Representation in Microsimulation Models

Eric Kai-Chung Wong, Wanrudee Isaranuwatchai, Joanna E. M. Sale, Andrea C. Tricco, Sharon E. Straus and David M. J. Naimark
Additional contact information
Eric Kai-Chung Wong: Faculty of Medicine, University of Toronto, Toronto, ON, Canada
Wanrudee Isaranuwatchai: Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
Joanna E. M. Sale: Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
Andrea C. Tricco: Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
Sharon E. Straus: Faculty of Medicine, University of Toronto, Toronto, ON, Canada
David M. J. Naimark: Faculty of Medicine, University of Toronto, Toronto, ON, Canada

Medical Decision Making, 2025, vol. 45, issue 3, 276-285

Abstract: Background In microsimulation models of diseases with an early, acute phase requiring short cycle lengths followed by a chronic phase, fixed short cycles may lead to computational inefficiency. Examples include epidemic or resource constraint models with early short cycles where long-term economic consequences are of interest for individuals surviving the epidemic or ultimately obtaining the resource. In this article, we demonstrate methods to improve efficiency in such scenarios. Furthermore, we show that care must be taken when applying these methods to epidemic or resource constraint models to avoid bias. Methods To demonstrate efficiency, we compared the model runtime among 3 versions of a microsimulation model: with short fixed cycles for all states (FCL), with dynamic cycle length (DCL) defined locally for each state, and with DCL features plus a discrete-event-like hybrid component. To demonstrate bias mitigation, we compared discounted lifetime costs for 3 versions of a resource constraint model: with a fixed horizon where simulation stops, with a fixed entry horizon beyond which new individuals could not enter the model, and with a fixed entry horizon plus a mechanism to maintain a constant level of competition for the resource after the horizon. Results The 3 versions of the microsimulation model had average runtimes of 515 (95% credible interval [CI]: 477 to 545; FCL), 2.70 (95% CI: 1.48 to 2.92; DCL), and 1.45 (95% CI: 1.26 to 2.61; DCL-pseudo discrete event simulation) seconds, respectively. The first 2 resource constraint versions underestimated costs relative to the constant competition version: $20,055 (95% CI: $19,000 to $21,120), $27,030 (95% CI: $24,680 to $29,412), and $33,424 (95% CI: $27,510 to $44,484), respectively. Limitations The magnitude of improvements in efficiency and reduction in bias may be model specific. Conclusion Changing time representation in microsimulation may offer computational advantages. Highlights Short cycle lengths may be required to model the acute phase of an illness but lead to computational inefficiency in a subsequent chronic phase in microsimulation models. A solution is to create state-specific cycle lengths so that cycle lengths change dynamically as the simulation progresses. Computational efficiency can be enhanced further by using a hybrid model containing discrete-event-simulation-like features. Hybrid models can efficiently handle events subsequent to exit from an epidemic or resource constraint model provided steps are taken to mitigate potential bias.

Keywords: microsimulation; discrete event simulation; hybrid models; open parallel models; computational efficiency (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0272989X251319808 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:45:y:2025:i:3:p:276-285

DOI: 10.1177/0272989X251319808

Access Statistics for this article

More articles in Medical Decision Making
Bibliographic data for series maintained by SAGE Publications ().