Covariate imbalance and adjustment for logistic regression analysis of clinical trial data

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

In logistic regression analysis for binary clinical trial data, adjusted treatment effect estimates are often not equivalent to unadjusted estimates in the presence of influential covariates. This article uses simulation to quantify the benefit of covariate adjustment in logistic regression. However, International Conference on Harmonization guidelines suggest that covariate adjustment be prespecified. Unplanned adjusted analyses should be considered secondary. Results suggest that if adjustment is not possible or unplanned in a logistic setting, balance in continuous covariates can alleviate some (but never all) of the shortcomings of unadjusted analyses. The case of log binomial regression is also explored.

Original languageEnglish (US)
Pages (from-to)1383-1402
Number of pages20
JournalJournal of Biopharmaceutical Statistics
Volume23
Issue number6
DOIs
StatePublished - Nov 2 2013

Keywords

  • Covariate
  • Imbalance
  • Logistic regression

ASJC Scopus subject areas

  • Statistics and Probability
  • Pharmacology
  • Pharmacology (medical)

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