Year: 2012
Type: dissertation
Abstract: This thesis considers a variety of statistical issues related to the design and analysis of clinical trials involving multiple \nlifetime events. The use of composite endpoints, multivariate survival methods with dependent censoring, and \nrecurrent events with dependent termination are considered. Much of this work is based on problems arising in oncology research. \n \n Composite endpoints are routinely adopted in multi-centre randomized trials designed to evaluate the effect of \nexperimental interventions in cardiovascular disease, diabetes, and cancer. Despite their widespread use, relatively \nlittle attention has been paid to the statistical properties of estimators of treatment effect based on composite \nendpoints. In Chapter 2 we consider this issue in the context of multivariate models for time to event data in which copula \nfunctions link marginal distributions with a proportional hazards structure. We then examine the asymptotic and \nempirical properties of the estimator of treatment effect arising from a Cox regression model for the time to the \nfirst event. We point out that even when the treatment effect is the same for the component events, the limiting value \nof the estimator based on the composite endpoint is usually inconsistent for this common value. The limiting value \nis determined by the degree of association between the events, the stochastic ordering of events, and the censoring \ndistribution. Within the framework adopted, marginal methods for the analysis of multivariate failure time data \nyield consistent estimators of treatment effect and are therefore preferred. We illustrate the methods by application \nto a recent asthma study. \n \n \nWhile there is considerable potential for more powerful tests of treatment effect when marginal methods are used, \nit is possible that problems related to dependent censoring can arise. \nThis happens when the occurrence of one type of event increases the risk of withdrawal from a study \nand hence alters the probability of observing events of other types. \nThe purpose of Chapter 3 is to formulate a model which reflects this type of mechanism, to evaluate \nthe effect on the asymptotic and finite sample properties of marginal estimates, and to examine the \nperformance of estimators obtained using flexible inverse probability weighted marginal estimating \nequations. Data from a motivating study are used for illustration. \n \n \n Clinical trials are often designed to assess the effect of therapeutic interventions on occurrence of recurrent events in \nthe presence of a dependent terminal event such as death. Statistical methods based on multistate analysis have considerable appeal in this setting since they can incorporate changes in risk with each event occurrence, a dependence between the recurrent event and \nthe terminal event and event-dependent censoring. To date, however, there has been limited methodology for the design of \ntrials involving recurrent and terminal events, and we addresses this in Chapter 4. Based on the asymptotic distribution of regression coefficients from a multiplicative intensity Markov regression model, we derive sample size formulae to address power requirements for both the recurrent and terminal event processes. Superiority and non-inferiority trial designs are dealt with. Simulation studies confirm that the designs satisfy the nominal power requirements in both settings, and an application to a trial evaluating the effect of a bisphosphonate on skeletal complications is given for illustration.
Source: UWSpace (University of Waterloo)
Author: Longyang Wu
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2608253316