Measuring continuous baseline covariate imbalances in clinical trial data. (April 2015)

• Record Type: Journal Article
• Authors: Ciolino, Jody D; Martin, Reneé H; Zhao, Wenle; Hill, Michael D; Jauch, Edward C; Palesch, Yuko Y
• Is Part Of: Statistical methods in medical research. Volume 24:Number 2(2015:Apr.)
• Page Start: 255
• Page End: 272
• Publication Date: 2015-04
• Subjects: baseline -- clinical trial -- covariate -- imbalance; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280211416038
• Languages: English
• ISSNs: 0962-2802

Abstract

This paper presents and compares several methods of measuring continuous baseline covariate imbalance in clinical trial data. Simulations illustrate that though the t-test is an inappropriate method of assessing continuous baseline covariate imbalance, the test statistic itself is a robust measure in capturing imbalance in continuous covariate distributions. Guidelines to assess effects of imbalance on bias, type I error rate and power for hypothesis test for treatment effect on continuous outcomes are presented, and the benefit of covariate-adjusted analysis (ANCOVA) is also illustrated.