Continuous covariate imbalance and conditional power for clinical trial interim analyses

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Oftentimes valid statistical analyses for clinical trials involve adjustment for known influential covariates, regardless of imbalance observed in these covariates at baseline across treatment groups. Thus, it must be the case that valid interim analyses also properly adjust for these covariates. There are situations, however, in which covariate adjustment is not possible, not planned, or simply carries less merit as it makes inferences less generalizable and less intuitive. In this case, covariate imbalance between treatment groups can have a substantial effect on both interim and final primary outcome analyses. This paper illustrates the effect of influential continuous baseline covariate imbalance on unadjusted conditional power (CP), and thus, on trial decisions based on futility stopping bounds. The robustness of the relationship is illustrated for normal, skewed, and bimodal continuous baseline covariates that are related to a normally distributed primary outcome. Results suggest that unadjusted CP calculations in the presence of influential covariate imbalance require careful interpretation and evaluation.

Original languageEnglish (US)
Pages (from-to)9-18
Number of pages10
JournalContemporary Clinical Trials
Issue number1
StatePublished - May 2014


  • Conditional power
  • Covariate adjusted analysis
  • Covariate imbalance

ASJC Scopus subject areas

  • Pharmacology (medical)


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