Inverse probability weighting for auxiliary variable dependent sampling in observational studies of Long COVID

Abstract

Selective testing based on values of auxiliary variables is an increasingly popular design strategy in observational studies and is ubiquitous in electronic health record data. Ignoring this underlying sampling mechanism can lead to biased estimation and erroneous scientific conclusions. Yet, rigorous analytic methods for accounting for two-phase sampling designs in observational settings remain under-utilized. Motivated by the Researching COVID to Enhance Recovery (RECOVER) Adult and Pediatric observational cohort studies, we describe common pitfalls and an approach for analysis of data collected via auxiliary variable dependent sampling.