Utility of automated data-adaptive propensity score method for confounding by indication in comparative effectiveness study in real world Medicare and registry data.

Background: Confounding by indication is a serious threat to comparative studies using real world data. We assessed the utility of automated data-adaptive analytic approach for confounding adjustment when both claims and clinical registry data are available.
Methods: We used a comparative study example of carotid artery stenting (CAS) vs. carotid endarterectomy (CEA) in 2005-2008 when CAS was only indicated for patients with high surgical risk. We included Medicare beneficiaries linked to the Society for Vascular Surgery's Vascular Registry >65 years old undergoing CAS/CEA. We compared hazard ratios (HRs) for death while adjusting for confounding by combining various 1) Propensity score (PS) modeling strategies (investigator-specified [IS-PS] vs. automated data-adaptive [ada-PS]); 2) data sources (claims-only, registry-only and claims-plus-registry); and 3) PS adjustment approaches (matching vs. quintiles-adjustment with/without trimming). An HR of 1.0 was used as a benchmark effect estimate based on CREST trial.
Results: The cohort included 1,999 CAS and 3,255 CEA patients (mean age 76). CAS patients were more likely symptomatic and at high surgical risk, and experienced higher mortality (crude HR = 1.82 for CAS vs. CEA). HRs from PS-quintile adjustment without trimming were 1.48 and 1.52 for claims-only IS-PS and ada-PS, 1.51 and 1.42 for registry-only IS-PS and ada-PS, and 1.34 and 1.23 for claims-plus-registry IS-PS and ada-PS, respectively. Estimates from other PS adjustment approaches showed similar patterns.
Conclusions: In a comparative effectiveness study of CAS vs. CEA with strong confounding by indication, ada-PS performed better than IS-PS in general, but both claims and registry data were needed to adequately control for bias.
Competing Interests: HK and HM are affiliated with the department of Healthcare Quality Assessment, University of Tokyo, which is a social collaboration department supported by the National Clinical Database, Johnson & Johnson KK, and Nipro Corporation. HK has received lecture fees from Chugai Pharmaceutical Co., Ltd., Johnson & Johnson KK, and consultancy fees from Mitsubishi Tanabe Pharm and EP Croit Co., Ltd. JJJ is a full-time employee of Regeneron Pharmaceuticals. This study was conducted prior to her employment at the company. SS has served as a member of the US FDA advisory committee and as a consultant for Pfizer Inc., Medtronic, and Merck. SS has also received research funding from the National Institutes of Health, the Cystic Fibrosis Foundation, Pfizer Inc., and Bristol-Myers Squibb. All other authors have no conflicts to declare. The above do not alter our adherence to Plos One policies on sharing data and materials.