Assessment of risk in longitudinal observational studies with time-varying drug exposures for treatment of a chronic disease

Abstract

Background: Drug exposures can be continuous or intermittent with several competing exposures which can influence the analysis of exposure-response relationships in longitudinal observational data. Purpose: We report the in-depth application of research methods employed in a nested case-control study to assess risk for fracture among persons with human immunodeficiency virus (HIV) infection treated with antiretroviral (ARV) therapy. These pharmacoepidemiological research methods, extrapolated from occupational epidemiology, permitted detailed exposure-response analysis in longitudinal observational data sources. Methods: Multidrug administrative prescription claims data were reformatted to create time-varying, nonoverlapping drug exposure histories that preserved the information in the original prescriptions. We applied research methods from occupational health to estimate drug-class (N=5) and drug-specific (N=29) exposure summary measures associated with fracture events. Results: Using pharmacy claims from the Clinformatics™ DataMart Multiplan (IMPACT) database, a product of OptumInsight Life Sciences, Inc., 1,929,067 prescription histories from 30,405 ARV-treated HIV subjects were restructured retaining gaps and removing overlaps and encompassments in the prescription histories. Most subjects were dispensed at least three different ARV drugs; over 75% had prescription histories with at least one gap in prescriptions and 23.5% had over 65 prescription claims. Drug exposure summary metrics were computed from the restructured claims to determine time-varying exposureresponse relationships for individual ARV drug exposures and fractures. Conclusions: We report a research method to assess drug exposure using administrative prescription claims in observational data that goes beyond dichotomous or cumulative exposures measurements of HIV drug classes and specific ARV drugs. The time-varying measures derived here are easily applicable to other observational data sources such as drug exposures for other chronic diseases where prescription histories are complex and evolve over time.