The implications of propensity score variable selection strategies in pharmacoepidemiology: an empirical illustration

• Published in: Pharmacoepidemiology and drug safety Vol. 20; no. 6; pp. 551 - 559
• Main Authors: Patrick, Amanda R., Schneeweiss, Sebastian, Brookhart, M. Alan, Glynn, Robert J., Rothman, Kenneth J., Avorn, Jerry, Stürmer, Til
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.06.2011; Wiley Subscription Services, Inc
• Subjects: Aged; Aged, 80 and over; Bias; confounding factors; Confounding Factors, Epidemiologic; Epidemiologic Methods; Female; Follow-Up Studies; Glaucoma - drug therapy; Hip Fractures - epidemiology; Humans; hydroxymethylglutaryl-CoA reductase inhibitors; Hydroxymethylglutaryl-CoA Reductase Inhibitors - therapeutic use; Male; pharmacoepidemiology; Pharmacoepidemiology - methods; Propensity Score; Proportional Hazards Models; Randomized Controlled Trials as Topic
• ISSN: 1053-8569; 1099-1557; 1099-1557
• DOI: 10.1002/pds.2098

ABSTRACT Purpose To examine the effect of variable selection strategies on the performance of propensity score (PS) methods in a study of statin initiation, mortality, and hip fracture assuming a true mortality reduction of < 15% and no effect on hip fracture. Methods We compared seniors initiating statins with seniors initiating glaucoma medications. Out of 202 covariates with a prevalence > 5%, PS variable selection strategies included none, a priori, factors predicting exposure, and factors predicting outcome. We estimated hazard ratios (HRs) for statin initiation on mortality and hip fracture from Cox models controlling for various PSs. Results During 1 year follow‐up, 2693 of 55 610 study subjects died and 496 suffered a hip fracture. The crude HR for statin initiators was 0.64 for mortality and 0.46 for hip fracture. Adjusting for the non‐parsimonious PS yielded effect estimates of 0.83 (95%CI:0.75–0.93) and 0.72 (95%CI:0.56–0.93). Including in the PS only covariates associated with a greater than 20% increase or reduction in outcome rates yielded effect estimates of 0.84 (95%CI:0.75–0.94) and 0.76 (95%CI:0.61–0.95), which were closest to the effects predicted from randomized trials. Conclusion Due to the difficulty of pre‐specifying all potential confounders of an exposure‐outcome association, data‐driven approaches to PS variable selection may be useful. Selecting covariates strongly associated with exposure but unrelated to outcome should be avoided, because this may increase bias. Selecting variables for PS based on their association with the outcome may help to reduce such bias. Copyright © 2011 John Wiley & Sons, Ltd.