Hiraku Kumamaru1, Joshua J Gagne2, Robert J Glynn2, Soko Setoguchi3, Sebastian Schneeweiss2. 1. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street (Suite 3030), Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. Electronic address: hik205@mail.harvard.edu. 2. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street (Suite 3030), Boston, MA, USA. 3. Duke Clinical Research Institute, Duke University, 2400 Pratt St, Durham, NC 27705, USA.
Abstract
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.
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.
Authors: Richard Wyss; Ben B Hansen; Alan R Ellis; Joshua J Gagne; Rishi J Desai; Robert J Glynn; Til Stürmer Journal: Am J Epidemiol Date: 2017-05-01 Impact factor: 4.897
Authors: Cheng Ju; Richard Wyss; Jessica M Franklin; Sebastian Schneeweiss; Jenny Häggström; Mark J van der Laan Journal: Stat Methods Med Res Date: 2017-12-11 Impact factor: 3.021
Authors: Cheng Ju; Susan Gruber; Samuel D Lendle; Antoine Chambaz; Jessica M Franklin; Richard Wyss; Sebastian Schneeweiss; Mark J van der Laan Journal: Stat Methods Med Res Date: 2017-09-22 Impact factor: 3.021