Optimal two-phase sampling designs for studies using error-prone electronic health record data with multiple parameters of interest.

Thomas M. Menino Convention & Exhibition Center 

Large observational datasets compiled from electronic health records are promising resources for medical research but are often affected by measurement error. Two-phase, multi-wave sampling with generalized raking (GR) offers a robust and efficient solution, though existing work has largely focused on estimation of a single target parameter. Motivated by two recent studies, we discuss extensions of this framework to the multiple parameters setting. We propose practical allocation strategies, including an integer-valued A-optimal method, and evaluate their performance through simulations and an application to a clinical HIV Cohort. Our results show that tailored multi-parameter designs for GR estimators yield marked efficiency gains over traditional case-control or IPW-optimal designs, with patterns that differ meaningfully from the single-parameter setting. These findings provide practical guidance for future two-phase studies using error-prone data.

Two-phase sampling

Generalized raking

Measurement error

Electronic health records

Survey design