Leveraging Historical Data to Evaluate an Intervention in a Pragmatic Cluster-Randomized Trial

Music City Center 

Strong evidence supports the effectiveness of pre-exposure prophylaxis (PrEP) in preventing human immunodeficiency virus infection; however, substantial provider- and patient-level barriers contribute to its underuse, disadvantaging racial, gender, and sexual minorities. Under a learning health system approach, a cluster-randomized trial evaluated the effect of linking a sexually transmitted infection testing bundle with a PrEP orderset to facilitate the prescription of PrEP. To rapidly assess this intervention, we seek novel methods to improve efficiency, and leveraging historical electronic health record data is a natural approach. However, current approaches to dynamic borrowing aren't able to account for the clustered nature of the data. We propose a method to dynamically borrow historical data from clinics where we expect similar trends in PrEP prescribing over time. We do this using a hierarchical Bayesian model with shrinkage via model averaging and the horseshoe prior. Our method results in increased efficiency relative to standard approaches, facilitating the rapid evaluation of this intervention and working to improve PrEP access in historically disadvantaged populations.

dynamic borrowing

hierarchical modeling

Bayesian model averaging

cluster randomized trial

biostatistics 

Caucus for Women in Statistics