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Activity Number: 564
Type: Contributed
Date/Time: Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract #313181 View Presentation
Title: Comparisons of Various Techniques in Propensity Score Estimation Using Simulation
Author(s): Jiaxiao Shi*+ and Wansu Chen
Companies: Kaiser Permanente and Kaiser Permanente
Keywords: Logistic Regression ; GBM ; GAM ; NNET ; TMLA
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 convenient for handling non-linear relationships between the exposure and covariates/confounders or interactions. Several non-parametric methods have been suggested as alternative techniques to estimate PS. They include generalized additive models (GAM), classification and regression trees (CART), generalized boosted models (GBM), neural networks (NNET) and targeted maximum estimation (TMLA). Simulation was conducted to compare the performance of LR, GAM, CART, GBM, NN, TMLA under varying degrees of non-linearity between the exposure and covariates, outcome rates and relative risks (RR). PS was applied into the outcome models by using regression adjustment. Our results showed that the recently developed TMLA had comparable performance compared with other non-parametric approaches in terms of bias and mean square errors.


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