Statistical Inference for the Treatment Effect in Cancer Clinical Trials
type: CreativeWork
description: Randomized clinical trials provide the best evidence on the effect of treatment studied. There are different types of measures on the treatment effect, depending on the endpoints of the trials. For a given measure, based on the data from clinical trials, various statistical procedures are available for the inference of the treatment effect in terms of this measure. In a cancer clinical trial with a time to an event as the endpoint, hazard ratio is a popular measure for the relative difference between treatment groups. Most current statistical inference procedures for hazard ratio rely on the proportional hazard assumption, which may not be applicable to practice when it does not hold. Nonparametric confidence intervals for the hazard ratio have been proposed based on the asymptotic normality of the kernel estimate for the hazard ratio, but they were found not very satisfactory in the simulation studies. In the first part of this thesis, the empirical likelihood method is used to construct the confidence interval for the time-dependent hazard ratio. The asymptotic distribution of the empirical likelihood ratio is derived and simulation studies are conducted to evaluate the proposed method. It was also argued that the measure of the relative treatment effect based on the hazard ratio may be difficult to understand by clinicians. An alternative measure called probabilistic index was suggested and the C-index was proposed to estimate this index. However, it was pointed out recently that the expected value of the estimate based on the C-index may be far removed from the true index. In the second part of this thesis, assuming a semi-parametric density ratio model, two new estimates based on respectively the conditional likelihood and weighted empirical likelihood are proposed. Associated confidence intervals are also derived based on the bootstrap re-sampling method. The proposed inference procedures are evaluated by Monte-Carlo simulations and applied to the analysis of …
abstract: Randomized clinical trials provide the best evidence on the effect of treatment studied. There are different types of measures on the treatment effect, depending on the endpoints of the trials. For a given measure, based on the data from clinical trials, various statistical procedures are available for the inference of the treatment effect in terms of this measure. In a cancer clinical trial with a time to an event as the endpoint, hazard ratio is a popular measure for the relative difference between treatment groups. Most current statistical inference procedures for hazard ratio rely on the proportional hazard assumption, which may not be applicable to practice when it does not hold. Nonparametric confidence intervals for the hazard ratio have been proposed based on the asymptotic normality of the kernel estimate for the hazard ratio, but they were found not very satisfactory in the simulation studies. In the first part of this thesis, the empirical likelihood method is used to construct the confidence interval for the time-dependent hazard ratio. The asymptotic distribution of the empirical likelihood ratio is derived and simulation studies are conducted to evaluate the proposed method. It was also argued that the measure of the relative treatment effect based on the hazard ratio may be difficult to understand by clinicians. An alternative measure called probabilistic index was suggested and the C-index was proposed to estimate this index. However, it was pointed out recently that the expected value of the estimate based on the C-index may be far removed from the true index. In the second part of this thesis, assuming a semi-parametric density ratio model, two new estimates based on respectively the conditional likelihood and weighted empirical likelihood are proposed. Associated confidence intervals are also derived based on the bootstrap re-sampling method. The proposed inference procedures are evaluated by Monte-Carlo simulations and applied to the analysis of …
author: Jiang, Shan
language: eng
keywords: Treatment effect, Clinical trials
date published: 2011-05-25T19:04:18Z
date modified: 2026-07-27T20:35:45Z
provider: Queens University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Queens University

PropertyValue
property ID: Handle
value: http://hdl.handle.net/1974/6522