A Methodological Review of Simulation Studies Published in Pharmacoepidemiology and Drug Safety

Simulation studies are used in pharmacoepidemiology for evaluating statistical 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.

We conducted a review of all papers published in the journal of Pharmacoepidemiology and Drug Safety (PDS) over the period 2017-2024. 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.

Among 42 simulation studies included, 34 (81%) were informing comparative effectiveness/safety studies. Twenty-two 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.

Simulations contributed a relatively small number of articles (3.2% of 1320) to PDS over 2017-2024. 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.