Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases

Area of Science:

• Health Informatics
• Biostatistics
• Pharmacoepidemiology

Background:

• Longitudinal healthcare claims databases are crucial for comparative medication studies but face bias from residual confounding.
• Existing simulation methods inadequately represent the complexity of large-scale pharmacoepidemiologic studies using secondary healthcare data.

Purpose of the Study:

• To develop and validate a statistical framework for creating realistic, replicated simulation datasets from empirical electronic healthcare claims data.
• To enable rigorous evaluation of confounding adjustment methods in complex pharmacoepidemiologic research.

Main Methods:

• A novel framework generates simulation datasets by resampling observed covariate and exposure data, preserving variable associations.
• Outcome data is simulated using a specified treatment effect and the empirical baseline hazard function.
• The framework was applied to Medicare and Caremark data for a statin use and cardiovascular outcomes study.

Main Results:

• Simulated datasets closely mirrored the complex structure of the empirical healthcare claims data.
• The framework successfully preserved covariate-exposure associations and allowed for investigator-specified treatment effects.
• Variable selection strategies for propensity score adjustment, including high-dimensional methods, were evaluated.

Conclusions:

• The developed framework provides a powerful tool for validating statistical methods in pharmacoepidemiology using complex healthcare claims data.
• This approach enhances the reliability of comparative safety and effectiveness studies by addressing limitations of traditional simulation methods.