Heterogeneous populations driven by clinical practice: A case example comparing enzalutamide (ENZA) and bicalutamide (BIC).

Abstract

e571 Background: Comparative real-world insurance claims data (RWD) analysis can be used to compare homogeneous cohorts (to avoid selection bias). Since the National Comprehensive Cancer Network guidelines recommend ENZA for metastatic castration-resistant prostate cancer (PC) and BIC for localized PC, it was of interest to evaluate these 2 heterogeneous cohorts as the ENZA cohort, being further advanced in the disease, has a worse prognosis than the BIC cohort. Here, we describe the results of a post hoc analysis on the ENZA and BIC cohorts to confirm the heterogeneity of demographics presented in a published study (Behl A et al. J Clin Oncol. 2016;34:235). Methods: The RWD captured enrollees with PC, claims for ENZA or BIC from 09/01/2012–12/31/2014, and at least 6 mo of continuous enrollment prior to the index date. After replicating methods from Behl et al using Truvan, we explored the balance of demographics in baseline periods greater than 6 mo. Differences in demographics and the post hoc analysis were compared. Results: Behl et al reported balanced age between the ENZA (73.1±10.0 y) and BIC (71.4±10.5 y) cohorts. However, 90% (531/592) of the ENZA cohort were metastatic vs 35% (1917/5524) of the BIC cohort. The Quan-Charlson comorbidity index was higher for the ENZA (5.6±2.1 y) than BIC (3.2±2.1 y) cohort. In the ENZA cohort, abiraterone acetate (ABI) or BIC utilization in the baseline period was higher (53%) than in the BIC cohort (0%). When the baseline period was greater than 6 mo, per our post hoc analysis, prior use of ABI or BIC increased to 68% for the ENZA cohort and remained unchanged in the BIC cohort. Conclusions: Despite results from the Behl analysis, the ENZA and BIC cohorts were dissimilar; ENZA patients had more advanced disease (metastatic disease; received ≥ 1 therapy) than BIC patients. Comparison of such heterogeneous populations can lead to biased estimates of treatment effects. Analyses lacking appropriate measures of prognostic variables that guide treatment decisions should be cautiously interpreted. Further analysis is required to identify best practices for using RWD and to address confounding driven by clinical practice.