Design and Validation of a Data Simulation Model for Longitudinal Healthcare Data

Evaluating performance characteristics of analytic methods developed to identify treatment effects in longitudinal healthcare data has been hindered by lack of an objective benchmark to measure performance. Relationships between drugs and subsequent treatment effects are not precisely quantified in real-world data, and simulated data offer potential to augment method development by providing data with known, measurable characteristics. However, the use of simulated data has been limited due to its inability to adequately reflect the complexities inherent in real-world databases that are necessary for effective method development. The goal of this study was to develop and evaluate a model for simulating longitudinal healthcare data that adequately captures these complexities. An empiric design was chosen that utilizes the characteristics of a real healthcare database as simulation input. This model demonstrates the potential for simulated data with known characteristics to adequately reflect complex relationships among diseases and treatments as recorded in healthcare databases.