Measuring continuous baseline covariate imbalances in clinical trial data

Jody D. Ciolino*, Reneé H. Martin, Wenle Zhao, Michael D. Hill, Edward C. Jauch, Yuko Y. Palesch

*Corresponding author for this work

Research output: Contribution to journalArticle

14 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)255-272
Number of pages18
JournalStatistical Methods in Medical Research
Issue number2
StatePublished - Apr 23 2015


  • baseline
  • clinical trial
  • covariate
  • imbalance

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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