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Abstract:
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In observational studies, generalized propensity score (GPS) based statistical methods have been proposed to estimate the average treatment effect (ATE) among multiple treatment groups. We investigate the GPS-based statistical methods to estimate treatment effects from two aspects. The first investigation is to obtain an optimal GPS estimation method among four competing GPS estimation methods by using a rank aggregation approach. We further examine whether the optimal GPS based doubly robust (DR) methods would improve the performance for estimating ATE. The second investigation is to examine whether the DR method could be improved if we ensemble outcome models. To that end, bootstrap method and rank aggregation method are used to obtain the ensemble optimal outcome model from several competing outcome models, and the resulting outcome model is incorporated into the DR method, resulting in an ensemble DR method. Extensive simulation results indicate that the the ensemble DR method provides the best performance in estimating the ATE. We used the MarketScan claims database to examine the treatment effects among three different bones and substitutes used for spinal fusion surgeries.
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