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Abstract Details

Activity Number: 178
Type: Contributed
Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
Sponsor: International Chinese Statistical Association
Abstract - #306581
Title: Assessment of Treatment Effect When Data Are Skewed and with Many Zeros
Author(s): Yue Wang*+
Companies: Astellas Pharma US
Address: , , ,
Keywords: propensity score ; bootstrap
Abstract:

Non-negative valued data containing large proportions of zero values and exhibiting potentially very large positive values can be encountered in many kinds of researches. Such characteristics present difficulties in obtaining valid inferences. Variable transformation may provide a very misleading, incomplete, and biased estimate of the impact of covariates on the untransformed scale. For randomized studies, comparison of means between groups can be made using nonparametric permutation test or simple bootstrap simulations. But when we do not have a randomized study or when we need to control for variables for more accurate estimates, we cannot apply permutation directly. We propose to use propensity score to divide the data and use nonparametric method to obtain inference about averaged effect. We use real life HRT study data to illustrate the method.


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