Background Cancer mortality maps are used by public health officials to identify areas of
excess and to guide surveillance and control activities. Quality of decision-making thus …

In this study, we consider a general, flexible, parametric hazard-based regression model for
censored lifetime data with covariates and term it the “general hazard (GH)” regression …

Survival analysis has received a great deal of attention as a subfield of Bayesian
nonparametrics over the last 50 years. In particular, the fitting of survival models that allow …

Background In the 1980s, an increase in mortality rates for prostate cancer was observed in
North America and developed European countries. In the 1990s, however, mortality rates …

Assessing regional differences in the survival of cancer patients is important but difficult
when separate regions are small or sparsely populated. In this paper, we apply a mixture …

Mixtures of Polya trees offer a very flexible nonparametric approach for modelling time-to-
event data. Many such settings also feature spatial association that requires further …

We describe generalized survival models, where g (S (t| z)), for link function g, survival S,
time t, and covariates z, is modeled by a linear predictor in terms of covariate effects and …

Understanding the factors that explain differences in survival times is an important issue for
establishing policies to improve national health systems. Motivated by breast cancer data …

As the treatments of cancer progress, a certain number of cancers are curable if diagnosed
early. In population‐based cancer survival studies, cure is said to occur when mortality rate …

Royston and Parmar (2002, Statistics in Medicine 21: 2175–2197) developed a class of
flexible parametric survival models that were programmed in Stata with the stpm command …