On the covariate-adjusted estimation for an overall treatment difference with data from a randomized comparative clinical trial.
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 no...
| Publicado en: | Biostatistics Vol. 13; no. 2; pp. 256 - 274 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Apr2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104534550&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104534550 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14654644 N58 jtl: Biostatistics issn: 14654644 maglogo: N pubinfo: dt: Apr2012 vid: 13 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104534550 NLM22294672 2011490472 10.1093/biostatistics/kxr050 NLM22294672 PMC3297822 104534550 ppf: 256 ppct: 18 formats: tig: atl: On the covariate-adjusted estimation for an overall treatment difference with data from a randomized comparative clinical trial. aug: au: Tian L Cai T Zhao L Wei LJ Tian, Lu Cai, Tianxi Zhao, Lihui Wei, Lee-Jen affil: Department of Health Research & Policy, Stanford University, Stanford, CA 94305, USA sug: subj: Clinical Trials Analysis of Variance Bias (Research) Statistics Data Analysis, Statistical Human Kaplan-Meier Estimator Liver Cirrhosis Drug Therapy Systems Analysis Chaos Theory Penicillamine Therapeutic Use Cox Proportional Hazards Model Treatment Outcomes ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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