Improving Efficiency of Inferences in Randomized Clinical Trials Using Auxiliary Covariates

Biometrics. 2008 Sep;64(3):707-15. doi: 10.1111/j.1541-0420.2007.00976.x. Epub 2008 Jan 11.


The primary goal of a randomized clinical trial is to make comparisons among two or more treatments. For example, in a two-arm trial with continuous response, the focus may be on the difference in treatment means; with more than two treatments, the comparison may be based on pairwise differences. With binary outcomes, pairwise odds ratios or log odds ratios may be used. In general, comparisons may be based on meaningful parameters in a relevant statistical model. Standard analyses for estimation and testing in this context typically are based on the data collected on response and treatment assignment only. In many trials, auxiliary baseline covariate information may also be available, and it is of interest to exploit these data to improve the efficiency of inferences. Taking a semiparametric theory perspective, we propose a broadly applicable approach to adjustment for auxiliary covariates to achieve more efficient estimators and tests for treatment parameters in the analysis of randomized clinical trials. Simulations and applications demonstrate the performance of the methods.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Acquired Immunodeficiency Syndrome / drug therapy
  • Analysis of Variance
  • Angina, Unstable / blood
  • Angina, Unstable / drug therapy
  • Anti-HIV Agents / therapeutic use
  • Anticoagulants / therapeutic use
  • Biometry / methods*
  • Humans
  • Linear Models
  • Logistic Models
  • Models, Statistical
  • Monte Carlo Method
  • Odds Ratio
  • Platelet Glycoprotein GPIIb-IIIa Complex / metabolism
  • Randomized Controlled Trials as Topic / statistics & numerical data*
  • Treatment Outcome


  • Anti-HIV Agents
  • Anticoagulants
  • Platelet Glycoprotein GPIIb-IIIa Complex