This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 360
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #308909
Title: Optimizing a Testing Procedure Based on Kernel Density Estimation for Comparing Two Treatments
Author(s): Sibabrata Banerjee*+ and Sunil Dhar and Farid Kianifard and Hanzhe Zheng and Venkata Sasikiran Goteti
Companies: Merck & Co., Inc. and New Jersey Institute of Technology and Novartis Pharmaceuticals Corporation and Merck & Co., Inc. and Merck & Co., Inc.
Address: 2000 Galloping Hill Road , Kenilworth, NJ, 07033,
Keywords: Clinical trial ; Effect size ; Probabilistic index ; Unbiased test ; Consistent test

Abstract: In a randomized clinical trial setting, if X and Y denote the random variables representing the responses of patients on two treatments A and B, respectively, the quantity P(Y>X) is an intuitive measure of efficacy. P(Y>X) is often called the probabilistic index. Kernel density estimation is a useful nonparametric method that has been utilized to estimate the probabilistic index. We use this same method as a hypothesis testing tool. We simulate data under different scenarios and estimate the power and type I error of this test.

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