Multivariate-adjusted pharmacoepidemiologic analyses of confidential information pooled from multiple health care utilization databases

Abstract

Purpose: Mandated post-marketing drug safety studies require vast databases pooled from multiple administrative data sources which can contain private and proprietary information. We sought to create a method to conduct pooled analyses while keeping information private and allowing for full confounder adjustment.

Methods: We propose a method based on propensity score (PS) techniques. A set of propensity scores are computed in each data-contributing center and a PS-adjusted analysis is then carried out on a pooled basis. The method is demonstrated in a study of the potentially negative effects of concurrent initiation of clopidogrel and proton pump inhibitors (PPIs) in four cohorts of patients assembled from North American claims data sources. Clinical outcomes were myocardial infarction (MI) hospitalization and hospitalization for revascularization procedure. Success of the method was indicated by equivalent performance of our PS-based method and traditional confounder adjustment. We also implemented and evaluated high-dimensional propensity scores and meta-analytic techniques.

Results: On both a pooled and individual cohort basis, we saw substantially similar point estimates and confidence intervals for studies adjusted by covariates and from privacy-maintaining propensity scores. The pooled, adjusted OR for MI hospitalization was 1.20 (95% confidence interval 1.03, 1.41) with individual variable adjustment and 1.16 (1.00, 1.36) with PS adjustment. The revascularization OR estimates differed by < 1%. Meta-analysis and pooling yielded substantially similar results.

Conclusions: We observed little difference in point estimates when we employed standard techniques or the proposed privacy-maintaining pooling method. We would recommend the technique in instances where multi-center studies require both privacy and multivariate adjustment.

Figure 1

Flow diagram of proposed data integration method