Bayesian model selection: analysis of a survival model with a surviving fraction

Using prior distributions to synthesize historical evidence: comments on the Goodman–Sladky case study of IVIg in Guillain–Barré syndrome

One feature of the Bayesian approach is that it provides methods for synthesizing what is known about a question of interest and provides a formalism based on the laws of probability for incorporating this auxiliary knowledge into the planning and the analysis of the next study. In this comment, we use elements of the Goodman-Sladky case study to illustrate (1) the use of Bayesian methods to quantify historical information about an intervention through the specification of a prior distribution, (2) an approach to the analysis of the sensitivity of the conclusions of a Bayesian analysis to the specification of the prior distribution, and (3) we comment on the role of research synthesis for combining information about an intervention from different data sources as a tool to help summarize evidence about the intervention.

Mixture and non-mixture cure fraction models based on the generalized modified Weibull distribution with an application to gastric cancer data

The cure fraction models are usually used to model lifetime time data with long-term survivors. In the present article, we introduce a Bayesian analysis of the four-parameter generalized modified Weibull (GMW) distribution in presence of cure fraction, censored data and covariates. In order to include the proportion of "cured" patients, mixture and non-mixture formulation models are considered. To demonstrate the ability of using this model in the analysis of real data, we consider an application to data from patients with gastric adenocarcinoma. Inferences are obtained by using MCMC (Markov Chain Monte Carlo) methods.

Bayesian statistics in medicine: a 25 year review

This review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies.