Accurate business failure prediction models would be extremely valuable to many industry sectors, particularly in financial investment and lending. The potential value of such models has been recently emphasised by the extremely costly failure of high profile businesses in both Australia and...

An important goal of research involving gene expression data for outcome prediction is to establish the ability of genomic data to define clinically relevant risk factors. Recent studies have demonstrated that microarray data can successfully cluster patients into low and high risk categories....

Dependent censoring occurs in longitudinal studies of recurrent events when the censoring time depends on the potentially unobserved recurrent event times. To perform regression analysis in this setting, we propose a semiparametric joint model that formulates the marginal distributions of the...

Cluster randomized trials, in which social units are selected as the units ofrandomization, have been increasingly used in the past three decades to evaluatethe effects of intervention. This thesis is devoted to design and analysis of clusterrandomized trials.Regarding design, we introduce a new...

We study unrepresentative observational data in survival analysis. The first paper focuses on proportional hazards regressionwhen observed subjects have different selection probabilities. Wedevelop methods which are applicable when the selectionprobabilities are unknown but estimated using...

In tumorigenicity experiments, a complication is that the time to event is generally not observed, so that the time to tumor is subject to interval censoring. One of the goals in these studies is to properly model the effect of dose on risk. Thus, it is important to have goodness of fit...

The research in this thesis focuses on methods for estimating the cumulative treatment effect on time to an event in the setting when the treatment-specific hazards are not proportional. In clinical studies of time to event data, non-proportional hazards are very common. The Cox model is...

In this article we investigate regression calibration methods to jointly model longitudinal and survival data using a semiparametric longitudinal model and a proportional hazards model. In the longitudinal model, a biomarker is assumed to follow a semiparametric mixed model where covariate...

We extend the proportional hazards model to a two-level model with a random intercept term and random coefficients. The parameters in the multilevel model are estimated by a combination of EM and Newton-Raphson algorithms. Even for samples of 50 groups, this method produces estimators of the...