Analysis of length-biased time-to-event data, which commonly arise in epidemiological cohort studies and cross-sectional surveys, has attracted considerable attention recently. Ignoring length-biased sampling often leads to severe bias in estimating the survival time in the general population. Existing work either completely ignore the covariate effects or use hazard or accelerated failure time...

We consider a nonparametric regression model with a parametric family of dependent variable transformations one of which induces additive covariate eeects. The asymptotic distribution of this regression estimate is given. The practical performance is investigated via an application. 1

One of the primary problems facing statisticians who work with survival data is the loss of information that occurs with right-censored data. This research considers trying to recover some of this endpoint information through the use of a prognostic covariate which is measured on each individual. We begin by defining a survival estimate which uses time-dependent covariates to more precisely get...

We study a flexible class of non-proportional hazard function regression models in which the influence of the covariates splits into the sum of a parametric part and a time-dependent nonparametric part. We develop a method of covariate selection for the parametric part by adjusting for the implicit fitting of the nonparametric part. Our approach is based on the general model selection methodolo...

Abstract Background Estimation that employs instrumental variables (IV) can reduce or eliminate bias due to confounding. In observational studies, instruments result from natural experiments such as the effect of clinician preference geographic distance on treatment selection. randomized studies randomization indicator is typically a valid instrument, especially if study blinded, e.g. no placeb...

SUMMARY Nonparametric Bayesian methods have proven to be extremely useful for providing flexible models that are capable of fitting an extraordinarily wide array of data sets. Two of their most natural uses are in providing distributions for random effects and in providing large classes of models that elaborate on a para-metric model. These models are appropriate for a great many data sets, all...

A population PK approach can be exploited throughout drug development to provide information about potential changes in the dose-exposure relationship for special populations (elderly, paediatrics, renal and hepatic impairment, etc) as an alternative to conducting specific studies. The usefulness of the population PK approach will, however, be highly dependent upon whether the inclusion/ exclus...

Benichou and Gail (1995, Biometrics 51, 182-194) describe methods for estimating the absolute risk of developing disease given a set of covariate values over a specified time interval from a case-control study within a cohort. The methods are most suitable for unmatched case-control studies, and are restricted to categorical covariates. Expanding on methods for estimating relative mortality fro...