OBJECTIVES

The objectives of this review were to identify studies conducting microsimulation modelling (MSM) in chronic diseases published between January 2010 to March 2021 and to evaluate methodologies used for probabilistic sensitivity analysis (PSA).

METHODS

MEDLINE and Google Scholar databases were searched. Data were extracted on policy questions, type of MSM, software used, PSA methodology, justification for number of iterations, sample sizes, and convergence analysis. All studies were screened and extracted by two independent reviewers. In case of disagreement between reviewers, a consensus was negotiated.

RESULTS

The systematic literature search identified 62 publications, 31 (50%) were “microsimulation” or “patient-level model”, 28 (45%) were “discrete-event” simulation, and three (5%) were “agent-based” models. There has been an increasing trend in the number of MSMs published, from five identified studies between 2010-2012 to 23 between 2019-2021. Out of the total number of MSMs identified, 13 (21%) studies conducted a PSA. Among these studies, only four performed second-order Monte Carlo analysis, none reported structural uncertainty, and only two provided justification for number of PSA replicates. No study used standardized software for validation of the PSA methodology.

CONCLUSIONS

While there has been an increasing number of studies applying MSM in healthcare policy evaluation for chronic diseases over the past decade, few conducted PSA, and fewer still reported second-order or structural uncertainty. More importantly, very few studies justified their PSA methodology and number of replicates used. There is a need for a uniform framework and standardized software packages in MSMs for conducting PSA in a manageable time.