The semi-parametric approach to the analysis of proportional hazards survival data
is relatively new, having been initiated in 1972 by Sir David Cox, who restricted its use
to hypothesis tests and confidence intervals for fixed effects in a regression setting.
Practitioners have begun to diversify applications of this model, constructing
residuals, modeling the baseline hazard, estimating median failure time, and analyzing
experiments with random effects and repeated measures. The main purpose of this
thesis is to show that working with an incompletely specified loglikelihood is more
fruitful than working with Cox's original partial loglikelihood, in these applications.
In Chapter 2, we show that the deviance residuals arising naturally from the partial
loglikelihood have difficulties detecting outliers. We demonstrate that a smoothed, nonparametric
baseline hazard partially solves this problem. In Chapter 3, we derive new
deviance residuals that are useful for identifying the shape of the baseline hazard. When
these new residuals are plotted in temporal order, patterns in the residuals mirror
patterns in the baseline hazard. In Chapter 4, we demonstrate how to analyze survival
data having a split-plot design structure. Using a BLUP estimation algorithm, we
produce hypothesis tests for fixed effects, and estimation procedures for the fixed
effects and random effects. / Graduation date: 1999
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/33302 |
| Date | 22 September 1998 |
| Creators | Derryberry, DeWayne R. |
| Contributors | Murtaugh, Paul A. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
Page generated in 0.0018 seconds