Comparison of high-dimensional confounder summary scores in comparative studies of newly marketed medications

Hiraku Kumamaru, Joshua J. Gagne, Robert J. Glynn, Soko Setoguchi Iwata, Sebastian Schneeweiss

Publication Date: 08/01/2016

Objective To compare confounding adjustment by high-dimensional propensity scores (hdPSs) and historically developed high-dimensional disease risk scores (hdDRSs) in three comparative study examples of newly marketed medications: (1) dabigatran vs. warfarin on major hemorrhage; (2) on death; and (3) cyclooxygenase-2 inhibitors vs. nonselective nonsteroidal anti-inflammatory drugs on gastrointestinal bleeds. Study Design and Setting In each example, we constructed a concurrent cohort of new and old drug initiators using US claims databases. In historical cohorts of old drug initiators, we developed hdDRS models including investigator-specified plus empirically identified variables and using principal component analysis and lasso regression for dimension reduction. We applied the models to the concurrent cohorts to obtain predicted outcome probabilities, which we used for confounding adjustment. We compared the resulting estimates to those from hdPS. Results The crude odds ratio (OR) comparing dabigatran to warfarin was 0.52 (95% confidence interval: 0.37–0.72) for hemorrhage and 0.38 (0.26–0.55) for death. Decile stratification yielded an OR of 0.64 (0.46–0.90) for hemorrhage using hdDRS vs. 0.70 (0.49–1.02) for hdPS. ORs for death were 0.69 (0.45–1.06) and 0.73 (0.48–1.10), respectively. The relative performance of hdDRS in the cyclooxygenase-2 inhibitors example was similar. Conclusion hdDRS achieved similar or better confounding adjustment compared to conventional regression approach but worked slightly less well than hdPS.