Pharmacoepidemiology simulation study practices: A methodological review

PurposeSimulation studies are used in pharmacoepidemiology for evaluating inferential methods in a controlled setting, whereby a known data-generating mechanism allows evaluation of the performance of different approaches and assumptions. This study aimed to review simulation studies performed in pharmacoepidemiology. MethodsWe conducted a review of all papers published in the journal of Pharmacoepidemiology and Drug Safety (PDS) over the period 2017 to 2024. We extracted data on study characteristics and key simulation choices such as the type of data generating mechanism used, inferential methods tested and simulation size. ResultsAmong 42 simulation studies included, 34 (81%) were informing comparative effectiveness/safety studies. 22 studies (52%) used simulation in the context of a clinical condition, and 36 (86%) used Monte-Carlo simulation. Inputs not derived from empirical data alone (n=22, 52%) or in combination with real-world data sources (n=19, 45%) were most often used for data generation. The complexity of simulations was often relatively low: although 31 studies (74%) generated data based on other covariates, time-dependent covariates (n=3) and effects (n=4) were rarely implemented. Bias was the most often used performance measure (n=26, 62%), although notably 18 studies (43%) did not report uncertainty in the method. ConclusionSimulations contributed a relatively small number of articles (3.2 % of 1320) to PDS over 2017 to 2024. Greater focus on evaluating methods and inferential approaches, using simulation studies that are appropriately complex given clinical realities may be beneficial to the pharmacoepidemiology field.