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Activity Number: 86
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #319069
Title: Composite Bootstrap Test with Counterintuitive Resampling Method to Compare Two Groups: An Application to Randomized Clinical Trials
Author(s): Alok Dwivedi* and Christopher Dodoo and Sada Nand Dwivedi and Rakesh Shukla
Companies: Texas Tech University Health Sciences Center El Paso and Texas Tech University Health Sciences Center El Paso and All India Institute of Medical Sciences and University of Cincinnati
Keywords: Bootstrap ; Randomized clinical trial ; Treatment effects ; Nonparametric test ; Distribution comparison

Randomized clinical trials are often used for evaluating treatment effect in evidence based medicine. Often due to distinct characteristics of patients, the effect of treatment on response may differ across subjects. In such cases, evaluating the effect of treatment on entire distribution of response may be of more interest instead of its mean. We propose an alternative bootstrap test with a counterintuitive method for comparing entire distribution between groups. This method includes student's t and rank t-test statistic for comparing means; first, second, and third quartile difference between groups for comparing first, second (median), and third quartiles respectively; and Kolmogorov-Smirnov test statistic for comparing different distributions between groups. For illustration, we utilized two randomized clinical trial datasets. The proposed bootstrap test provided the most comprehensive description and interesting treatment effects in both datasets which could not be captured by standard data analysis. The proposed approach may also be applicable for other designs where investigators might be interested in comparing between the two groups considering entire distributions.

Authors who are presenting talks have a * after their name.

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