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Activity Number: 82
Type: Contributed
Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract #315072 View Presentation
Title: Analysis of Paired Data in Randomized Clinical Trials: An Application of Quantile Regression
Author(s): Alok Dwivedi* and Indika Mallawaarachchi and Michael Privitera and Sheryl Haut and Sada Nand Dwivedi and Rakesh Shukla and Patrick Tarwater
Companies: Texas Tech University Health Sciences Center and Texas Tech University Health Sciences Center and University of Cincinnati and Montefiore Medical Center and All India Institute of Medical Sciences and University of Cincinnati and Texas Tech University Health Sciences Center
Keywords: Clinical trial ; Paired data ; Quantile regression ; Percentage change ; Absolute change
Abstract:

Clinicians are often interested in comparing relative changes in quantitative measures from baseline between the treatment groups in clinical trials. Such comparison between groups can be assessed considering three ways: nonparametric/parametric methods of analysis of absolute change and percentage change in outcome from baseline and analysis of covariance (ANCOVA) for post-treatment outcome with baseline measure as a covariate. These analyses may provide inconsistent results if pre/post treatment data follow a non-normal distribution. In such situations, we propose use of quantile regression (QR). We have illustrated the use of QR in analyzing paired data using an ongoing clinical trial study on 42 patients (20 in group1 and 22 in group2) with seizure frequency (SF) as an outcome. Percentage change, absolute change and ANCOVA analyses of SF provided inconsistent results that might be due to skewed distribution of SF. QR guided absolute change, percentage change, and ANCOVA analyses of SF showed that SF was significantly different between treatment groups in patients with high baseline SF. QR guided analysis should be used for non-normally paired data in clinical trial studies.


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