Recently, a Bayesian paradigm was constructed for Phase I trial designs that allows for theevaluation and comparison of several nested treatment schedules, each consisting of a sequence ofadministration times. In contrast to traditional Phase I trial designs that seek to find a maximum...

Hierarchical model such as Fay–Herriot (FH) model is often used in small area estimation. The method might perform well overall but is vulnerable to outliers. We propose a robust extension of the FH model by assuming the area random effects follow a t distribution with an unknown...

In an individually matched case–control study, effects of potential risk factors are ascertained through conditional logistic regression (CLR). Extension of CLR to situations with multiple disease or reference categories has been made through polychotomous CLR and is shown to be more efficient...

We examine several variance estimators for cumulative incidence estimators that have been proposed over time, some of which are derived from asymptotic martingale or counting process theory, and some of which are developed from the moments of the multinomial distribution. There is little...

A standard approach to the analysis of skewed response data with concomitant information is to use a log-transformation to normalize the distribution of the response variable and then conduct a log- regression analysis. However, the mean response at original scale is often of interest....

This research was motivated by a desire to model the progression of a chronic disease through various disease stages when data are not available to directly estimate all the transition parameters in the model. This is a common occurrence when time and expense make it infeasible to follow a...

Often in biomedical studies, the event of interest is recurrent and within-subject events cannot usually be assumed independent. In semi-parametric estimation of the proportional rates model, a working independence assumption leads to an estimating equation for the regression parameter vector,...

In biomedical studies, the event of interest is often recurrent and within-subject events cannot usually be assumed independent. In addition, individuals within a cluster might not be independent; for example, in multi-center or familial studies, subjects from the same center or family might be...

In a common ROC study design, several readers are asked to rate diagnostics of the same cases processed under different modalities. We describe a Bayesian hierarchical model that facilitates the analysis of this study design by explicitly modelling the three sources of variation inherent to it....