Quantifying the cost in power of ignoring continuous covariate imbalances in clinical trial randomization

Jody Ciolino*, Wenle Zhao, Renee' Martin, Yuko Palesch

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Motivated by potentially serious imbalances of continuous baseline covariates in clinical trials, we investigated the cost in statistical power of ignoring the balance of these covariates in treatment allocation design for a logistic regression model. Based on data from a clinical trial of acute ischemic stroke treatment, computer simulations were used to create scenarios varying from the best possible baseline covariate balance to the worst possible imbalance, with multiple balance levels between the two extremes. The likelihood of each scenario occurring under simple randomization was evaluated. The power of the main effect test for treatment was examined. Our simulation results show that the worst possible imbalance is highly unlikely, but it can still occur under simple random allocation. Also, power loss could be nontrivial if balancing distributions of important continuous covariates were ignored even if adjustment is made in the analysis for important covariates. This situation, although unlikely, is more serious for trials with a small sample size and for covariates with large influence on primary outcome. These results suggest that attempts should be made to balance known prognostic continuous covariates at the design phase of a clinical trial even when adjustment is planned for these covariates at the analysis.

Original languageEnglish (US)
Pages (from-to)250-259
Number of pages10
JournalContemporary Clinical Trials
Volume32
Issue number2
DOIs
StatePublished - Mar 1 2011

Keywords

  • Clinical trial
  • Covariate
  • Power
  • Randomization

ASJC Scopus subject areas

  • Pharmacology (medical)

Fingerprint Dive into the research topics of 'Quantifying the cost in power of ignoring continuous covariate imbalances in clinical trial randomization'. Together they form a unique fingerprint.

Cite this