![]() ![]() In the case where sufficient longitudinal data is available, statistical methods such as vector autoregressive or multivariate generalized linear mixed models may be used to estimate risk factor distributions over time. ![]() Microsimulations must therefore capture serial correlations in risk factors over time to make accurate predictions about future disease risks or compare the likely effectiveness of alternative interventions. Microsimulations can not only simulate the cohort average values of risk factors and health outcomes, but also simulate the whole risk factor distribution, which is particularly useful for identifying high-risk cases for intervention targeting. Microsimulation models track simulated cohorts of individuals in terms of their risk factors and health outcomes over time, and have become increasingly popular for their flexibility and scalability in simulating diseases as varied as cancer, cardiovascular disease, tuberculosis, and HIV –. ![]()
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