Optimal sampling for generalized raking estimators to address data quality in a multinational three-phase study of frailty among people with HIV.

Thomas M. Menino Convention & Exhibition Center 

The increasing number of observational studies in which electronic health records(EHR) are the primary source of data has led to a greater focus on methods to correct for error-prone data. More recently, three-phase studies have been employed for measurement error correction in which there are error-prone EHR data for the entire cohort (phase-1), audited (i.e., chart-reviewed) EHR data for a sub-cohort (phase-2), and reference standard data not part of routine care that is collected for a sub-sample of patients (phase-3). An ongoing study is focused on factors associated with frailty among people living with HIV (PWH) in Latin America and East Africa. Researchers have previously collected phase-1 and phase-3 data, along with phase-2 data for records in phase-3. Generalized raking (GR) estimators that incorporate phase-1 and phase-2 data by calibrating inverse probability weighted (IPW) estimators with estimated influence functions will be used for our analyses. We can improve the efficiency of GR estimators by collecting additional phase-2 data, which is relatively inexpensive. We derive optimal phase-2 sampling strategies. We show the impact of our sampling designs relative to comparators with extensive simulations, and we illustrate our design applied to the study of frailty in PWH.