On the covariate-adjusted estimation for an overall treatment difference with data from a randomized comparative clinical trial

Lu Tian, Tianxi Cai, Lihui Zhao, Lee Jen Wei*

*Corresponding author for this work

Research output: Contribution to journalArticle

18 Scopus citations

Abstract

To estimate an overall treatment difference with data from a randomized comparative clinical study, baseline covariates are often utilized to increase the estimation precision. Using the standard analysis of covariance technique for making inferences about such an average treatment difference may not be appropriate, especially when the fitted model is nonlinear. On the other hand, the novel augmentation procedure recently studied, for example, by Zhang and others (2008. Improving efficiency of inferences in randomized clinical trials using auxiliary covariates. Biometrics 64, 707-715) is quite flexible. However, in general, it is not clear how to select covariates for augmentation effectively. An overly adjusted estimator may inflate the variance and in some cases be biased. Furthermore, the results from the standard inference procedure by ignoring the sampling variation from the variable selection process may not be valid. In this paper, we first propose an estimation procedure, which augments the simple treatment contrast estimator directly with covariates. The new proposal is asymptotically equivalent to the aforementioned augmentation method. To select covariates, we utilize the standard lasso procedure. Furthermore, to make valid inference from the resulting lasso-type estimator, a cross validation method is used. The validity of the new proposal is justified theoretically and empirically. We illustrate the procedure extensively with a well-known primary biliary cirrhosis clinical trial data set.

Original languageEnglish (US)
Pages (from-to)256-273
Number of pages18
JournalBiostatistics
Volume13
Issue number2
DOIs
StatePublished - Apr 2012

Keywords

  • ANCOVA
  • Cross validation
  • Efficiency augmentation
  • Mayo PBC data
  • Semi-parametric efficiency

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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