Quantitative Issues in Cancer Research Working Seminar

Luke Benz
Doctoral Student, Department of Biostatistics, Harvard University

“Understanding Missing Data when Emulating Target Trials in EHR-Based Observational Studies”
ABSTRACT: In recent years, the target trial emulation framework has developed as a framework for helping researchers mitigate or avoid potential biases in observational studies. In simplest terms, target trial emulation requires researchers to specify the protocol for an ideal clinical trial they would run if possible, and subsequently establish an analogous version of the protocol for the observational study that adheres as closely as possible to that of the target trial.

A critical component of this target trial emulation framework is specifying the eligibility criteria for inclusion in the study. Electronic health record databases serve as a useful data source for observational analyses. However, as EHR are collected for billing purposes rather than any particular clinical question, useful information for statistical analyses may be unavailable. In particular, when using EHR databases to emulate target trials, it is frequently the case that subjects’ eligibility status can not be ascertained due to missing data in the covariates that comprise the inclusion criteria for the study. Nearly every observational analysis under the target trial emulation framework excludes all subjects with missing eligibility data, yet this could plausibly introduce selection bias, particularly when a sequence of emulated trials are pooled to increase power.

In this work, I will outline ongoing work on building infrastructure for several simulation studies to better understand settings where excluding subjects with missing eligibility data is problematic, and potential solutions. These simulation studies are motivated by the study of long term effects of Bariatric surgery, and simulation settings are informed by prior analyses conducted on EHR-based studies on the DURABLE cohort at Kaiser Permanente.