Thesis (Ph. D.)--University of Washington, 1989. / Vita. Includes bibliographical references ([217]-228).

Thesis (Ph. D.)--University of Washington, 1992. / Vita. Includes bibliographical references (leaves [135]-138).

Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 97-102).

Source: Dissertation Abstracts International, Volume: 54-08, Section: B, page: 3941. Supervisor: Robert J. Hardy. Includes bibliographical references (leaves 109-114).

PROPHAZ is a computer program created for the analysis of survival data using the general proportional hazards model. It was designed specifically for the situation in which the underlying hazard function may be estimated from the mortality experience of a large reference population, but may be used for other problems as well. Input for the program includes the variables of interest as well as the information necessary for estimating the hazard function (demographic and mortality data). Regression coefficients for the variables of interest are obtained iteratively using the Newton-Raphson method. Utilizing large sample asymptotic theory, χ2 statistics are derived which may be used to test hypotheses of the form Cβ = 0. Input format is completely flexible for the variables of interest as well as the mortality data.

No description available.

No description available.

The Cox proportional hazards model is a standard tool for modelling the effect of covariates on time to event in the presence of censoring. The appropriateness of this model is conditioned by the validity of the proportional hazards assumption. The assumption is explained in the thesis and methods for its testing are described in detail. The tests are implemented in R, including self-written version of the Lin- Zhang-Davidian test. Their application is illustrated on medical data. The ability of the tests to reveal the violation of the proportional hazards assumption is investigated in a simulation study. The results suggest that the highest power is attained by the newly implemented Lin-Zhang-Davidian test in most cases. In contrast, the weighted version of the Lin-Wei-Ying test was found to have inadequate size for low sample sizes.

The two and k-sample tests of equality of the survival distributions against the alternatives including cross-effects of survival functions, proportional and monotone hazard ratios, are given for the right censored data. The asymptotic power against approaching alternatives is investigated. The tests are applied to the well known chemio and radio therapy data of the Gastrointestinal Tumor Study Group. The P-values for both proposed tests are much smaller then in the case of other known tests. Differently from the test of Stablein and Koutrouvelis the new tests can be applied not only for singly but also to randomly censored data.

Master of Science / Department of Statistics / James J. Higgins / The time for an event to take place in an individual is called a survival time. Examples include the time that an individual survives after being diagnosed with a terminal illness or the time that an electronic component functions before failing. A popular parametric model for this type of data is the Weibull model, which is a flexible model that allows for the inclusion of covariates of the survival times. If distributional assumptions are not met or cannot be verified, researchers may turn to the semi-parametric Cox proportional hazards model. This model also allows for the inclusion of covariates of survival times but with less restrictive assumptions. This report compares estimates of the slope of the covariate in the proportional hazards model using the parametric Weibull model and the semi-parametric Cox proportional hazards model to estimate the slope. Properties of these models are discussed in Chapter 1. Numerical examples and a comparison of the mean square errors of the estimates of the slope of the covariate for various sample sizes and for uncensored and censored data are discussed in Chapter 2. When the shape parameter is known, the Weibull model far out performs the Cox proportional hazards model, but when the shape parameter is unknown, the Cox proportional hazards model and the Weibull model give comparable results.