Modelling multiple disease risk factors for microsimulation studies: a review of methods
Abstract
Longitudinal microsimulation models to evaluate policies and scenarios for chronic disease risk reduction typically involve simulating multiple risk factors for a synthetic population over time. Various statistical methods have been used to accomplish this, but the principles behind them have never been comprehensively reviewed or critically compared. We describe and review the range of methods that have been used to simulate multiple risk factors over time, given either longitudinal or repeated cross-sectional data, highlight those which are most practically applicable and flexible, and introduce some useful variants of them. We describe procedures that can be used to check and compare these methods in practice based on fit to data, and present a case study where the methods are used and compared, based on a microsimulation model to evaluate mid-life cardiovascular health checks. We conclude that a useful range of procedures are available, and this paper improves the knowledge base to support their use in models to inform health policy.
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BibTeX
@misc{church2026modelling,
title = {{Modelling multiple disease risk factors for microsimulation studies: a review of methods}},
author = {Oliver Church and Christopher Jackson},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.15353},
eprint = {2609.15353},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.15353},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Modelling multiple disease risk factors for microsimulation studies: a review of methods
AU - Oliver Church
AU - Christopher Jackson
PY - 2026
DA - 2026/09/14
PB - arXiv
DO - 10.48550/arxiv.2609.15353
UR - https://arxiv.org/abs/2609.15353
AB - Longitudinal microsimulation models to evaluate policies and scenarios for chronic disease risk reduction typically involve simulating multiple risk factors for a synthetic population over time. Various statistical methods have been used to accomplish this, but the principles behind them have never been comprehensively reviewed or critically compared. We describe and review the range of methods that have been used to simulate multiple risk factors over time, given either longitudinal or repeated cross-sectional data, highlight those which are most practically applicable and flexible, and introduce some useful variants of them. We describe procedures that can be used to check and compare these methods in practice based on fit to data, and present a case study where the methods are used and compared, based on a microsimulation model to evaluate mid-life cardiovascular health checks. We conclude that a useful range of procedures are available, and this paper improves the knowledge base to support their use in models to inform health policy.
ER -