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Improving the Efficiency of Tests and Estimators of Treatment Effect with Auxiliary Covariates in the Presence of Censoring

In most randomized clinical trials, the primary response variable, for example, the survival time, is not observed directly after the patients enroll in the study but rather observed after some period of time (lag time). It is often the case that such a response variable is missing for some patients because of censoring such as administrative censoring that occurs when the study ends before all the patients had the opportunity to observe their response but also censoring may result from patient dropout. It is often assumed that censoring occurs at random which is referred to as noninformative censoring; however, in many cases such an assumption may not be reasonable. If the missing data are not analyzed properly, the estimate or test for the treatment effect may be biased. In this paper, we considered two situations. In the first situation, we only consider the special case where the censoring time is noninformative and the survival time itself the time-lagged response. We use semiparametric theory to derive a class of consistent and asymptotically normal estimators for the unconditional log-hazard ratio parameter. The prognostic auxiliary covariates are used to derive estimators that are more efficient than the traditional maximum partial likelihood estimator and the corresponding Wald tests are more powerful than the logrank test. In the second situation, we extended the results under the first situation to a general case where the censoring time can be informative and the time-lagged response can be any type. We also use the semiparametric theory to derive a class of consistent and asymptotic estimator for the treatment effect estimator. The prognostic baseline auxiliary covariates and post-treatment auxiliary covariates, which may be time-dependent, are also used to derive estimators that both account for informative censoring and are more efficient then the estimators which do not consider the auxiliary covariates.

Identiferoai:union.ndltd.org:NCSU/oai:NCSU:etd-05082007-235129
Date30 May 2007
CreatorsLu, Xiaomin
ContributorsMarie Davidian, Anastasios A. Tsiatis, Hao Zhang, Wenbin Lu
PublisherNCSU
Source SetsNorth Carolina State University
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://www.lib.ncsu.edu/theses/available/etd-05082007-235129/
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