Introduction The Premier Perspective hospital billing database provides a promising data source for studies of inpatient medication use. However, in-hospital recording of confounders is limited, and incorporating linked healthcare claims data available for a subset of the cohort may improve confounding control. We investigated methods capable of adjusting for confounders measured in a subset, including complete case analysis, multiple imputation of missing data, and propensity score (PS) calibration. Methods Methods were implemented in an example study of adults in Premier undergoing percutaneous coronary intervention (PCI) in 2004–2008 and exposed to either bivalirudin or heparin. In a subset of patients enrolled in UnitedHealth for at least 90 days before hospitalization, additional confounders were assessed from healthcare claims, including comorbidities, prior medication use, and service use intensity. Diagnostics for each method were evaluated, and methods were compared with respect to the estimates and confidence intervals of treatment effects on repeat PCI, bleeding, and in-hospital death. Results Of 210,268 patients in the hospital-based cohort, 3240 (1.5 %) had linked healthcare claims. This subset was younger and healthier than the overall study population. The linked subset was too small for complete case evaluation of two of the three outcomes of interest. Multiple imputation and PS calibration did not meaningfully impact treatment effect estimates and associated confidence intervals. Conclusions Despite more than 98 % missingness on 24 variables, PS calibration and multiple imputation incorporated confounders from healthcare claims without major increases in estimate uncertainty. Additional research is needed to determine the relative bias of these methods.
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