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