title: On the covariate-adjusted estimation for an overall treatment difference with data from a randomized comparative clinical trial.
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.
authors.affiliation: Department of Health Research & Policy, Stanford University, Stanford, CA 94305, USA.
authors.forename: Lu
authors.initials: L
authors.lastname: Tian
authors.forename: Tianxi
authors.initials: T
authors.lastname: Cai
authors.forename: Lihui
authors.initials: L
authors.lastname: Zhao
authors.forename: Lee-Jen
authors.initials: LJ
authors.lastname: Wei
mesh_terms: D000704:Analysis of Variance; D015982:Bias; D056808:Biostatistics; D003627:Data Interpretation, Statistical; D006801:Humans; D053208:Kaplan-Meier Estimate; D008105:Liver Cirrhosis, Biliary / Q000188:drug therapy; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D010396:Penicillamine / Q000627:therapeutic use; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D016896:Treatment Outcome
chemical_list: D010396:Penicillamine