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Activity Number: 32
Type: Contributed
Date/Time: Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract - #310081
Title: Performance of Various Propensity Score Estimation Techniques for Estimating Relative Risks: A Simulation Study
Author(s): Jiaxiao Shi*+ and Wansu Chen
Companies: Kaiser Permanente and Kaiser Permanente
Keywords: Logistic Regression ; CART ; GBM ; NNET
Abstract:

Propensity score (PS) method has been increasingly used to address confounding issues when evaluating causal effects. The score is often estimated using the logistic regression model (LR), an approach that is not flexible for handling non-linear relationships between the exposure and covariates used to predict the score. Several non-parametric methods have been suggested as alternative techniques to estimate PS. They include classification and regression trees (CART), generalized boosted models (GBM) and neural networks (NNET). Simulation was conducted to compare the performance of LR, CART, GBM, and NN under varying degrees of non-linearity between the exposure and covariates, and at various levels of outcome rates and relative risks (RR). PS was applied into the outcome models by using regression adjustment. Our results showed that CART performed the best in both bias and mean square error (MSE) when outcome rate was low (1%). When outcome rate was not extremely rare (10%), the biases and MSEs for all other techniques were comparable and were smaller than those of CART. Users may need to be aware of the outcome rate when selecting a PS method.


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