Measuring continuous baseline covariate imbalances in clinical trial data
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Updated: May 29, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Jody D Ciolino1, Reneé H Martin2, Wenle Zhao2
1Division of Biostatistics and Epidemiology, 135 Cannon Street, Suite 303, Medical University of South Carolina, Charleston, SC, USA. jodycio@gmail.com.
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.
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