Semiparametric Methods for Analysis of Randomized Clinical Trials and Arbitrarily Censored Time-to-event Data.

Covariate Adjustement;Semiparametrics;Survival Anaysis;Clinical Trials

Zhang, Min ; Wenbin Lu, Committee Member,Marie Davidian, Committee Chair,Anastasios A. Tsiatis, Committee Co-Chair,Daowen Zhang, Committee Member,Zhang, Min ; Wenbin Lu ; Committee Member ; Marie Davidian ; Committee Chair ; Anastasios A. Tsiatis ; Committee Co-Chair ; Daowen Zhang ; Committee Member

University:North Carolina State University

关键词: Covariate Adjustement;    Semiparametrics;    Survival Anaysis;    Clinical Trials;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/5798/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

This dissertation includes two parts. In part one, using the theory of semiparametrics, we develop a general approach to improving efficiency of nferences in randomized clinical trials using auxiliary covariates. In part two, we study "smooth" semiparametric regression analysisfor arbitrarily censored time-to-event data.The primary goal of a randomized clinical trial is to make comparisons among two or more treatments.For example, in a two-arm trial withcontinuous response, the focus may be on the difference in treatment means; with more than two treatments, the comparison may be based onpairwise differences.With binary outcomes, pairwise odds-ratios or log-odds ratios may be used.In general, comparisons may be based onmeaningful parameters in a relevant statistical model.Standard analyses for estimation and testing in this context typically arebased 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 testsfor treatment parameters in the analysis of randomized clinical trials.Simulations and applications demonstrate the performance ofthe methods.A general framework for regression analysis of time-to-event data subject to arbitrary patterns of censoring is proposed.The approach is relevant when the analyst is willing to assume that distributions governing model components that are ordinarily left unspecified in popular semiparametric regression models, such as the baseline hazard function in the proportional hazards model, have densities satisfyingmild "smoothness" conditions.Densities are approximated by a truncated series expansion that, for fixed degree of truncation, results in a "parametric" representation, which makeslikelihood-based inference coupled with adaptive choice of the degree of truncation, and hence flexibility of the model, computationally andconceptually straightforward with data subject to any pattern of censoring.The formulation allows popular models, such as the proportional hazards, proportional odds, and accelerated failure timemodels, to be placed in a common framework; provides a principled basis for choosing among them; and renders useful extensions of themodels straightforward.The utility andperformance of the methods are demonstrated via simulations and by application to data fromtime-to-event studies.