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Activity Number: 371
Type: Contributed
Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #317038 View Presentation
Title: Using Simulation to Compare Performance of Various Prognostic Propensity Scores, Propensity Scores, and Inverse Probability Treatment Weighting (IPTW) Using Propensity Scores
Author(s): In-Lu Liu* and Jiaxiao Shi and Wansu Chen
Companies: Kaiser Permanente and Kaiser Permanente Southern California and Kaiser Permanente Southern California
Keywords: Disease risk score ; Propensity score ; Prognostic Propensity score ; Inverse probability treatment weighting (IPTW) ; Log-binomial model
Abstract:

Propensity score (PS) and disease risk score (DRS) are popular covariates balancing techniques for observational studies. However, few has been reported on the performance of prognostic propensity score (DRS-PS) method which combines the two techniques together in covariate adjustment setting. We conducted simulations to evaluate the performance of three DRS-PS models, a probability-based PS models, and an inverse probability treatment weighting using PS model (IPTW). We observed, in general, IPTW method out-performed other methods models, particularly in the scenarios with rare outcome, lower exposure rates and high relative risk (RR). Among three DRS-PS models, the full-cohort DRS-PS method usually performed better than the other two methods. It sometimes even out performed PS and IPTW. DRS-PS performance generally improved when outcome and exposure rates increased, and RR decreased. IPTW and the full-cohort DRS-PS method usually had better mean squared errors than PS and other two DRS-PS methods. Users need to be aware of outcome and exposure rate before choosing an appropriate method to estimate RR.


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