Many cancer studies (animal, clinical and epidemiological) lead to event-
time data in which multiple events, possibly of different types can occur
to each subject or to each of a group of related subjects, for example
animals born in the same litter. Examples of such data include time to
tumor detection, time from remission to relapse into an acute disease
phase, and times to discontinuation of experimental medications. Methods
for the statistical analysis of such data will need to take into account
heterogeneity between subjects or between groups of related subjects. This
can be achieved by the incorporation of additional random effects into the
standard survival models. The present research focusses on models
involving "frailties", unobserved random proportionality factors applied
to the hazard function. Methods of parametric, semiparametric and
nonparametric estimation in such models will be investigated and applied
to multiple event data arising from studies of cancer and other diseases.
Bayesian methods, often involving the use of the Gibbs sampler will be
considered as well as classical frequentist techniques. Emphasis will be
placed on the formulation of models in such a way that covariate effects
can be interpreted both conditionally on the random effect, and
Frailty models will be used to investigate selection biases in
epidemiologic studies and in the analysis of compliance data in clinical
trials. Methods will be proposed for analysis of data when there is more
than one plausible time scale, for example in a mortality study of
asbestos workers one may want to use both calendar time and cumulative
exposure to asbestos dust. In a study of the development of tumors in
experimental animals, both the time from the start of the study and the
time since the last tumor may be relevant. A general class of
multiplicative intensity models involving multiple time scales will be
Characterizations of multivariate' survival distributions will be explored
with a view to deriving graphical methods of assessing goodness of fit.
The performance of a new algorithm for estimating a bivariate survival
distribution subject to a general pattern of censoring will be
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