Background. Investing efficiently in future research to improve policy decisions is an
important goal. Expected value of sample information (EVSI) can be used to select the …

Cancer prediction models are becoming ubiquitous, yet we generally have no idea whether
they do more good than harm. This is because current statistical methods for evaluating …

The paper demonstrates how cost-effectiveness decision analysis may be implemented from
a Bayesian perspective, using Markov chain Monte Carlo simulation methods for both the …

Background Logistic random effects models are a popular tool to analyze multilevel also
called hierarchical data with a binary or ordinal outcome. Here, we aim to compare different …

Background. Diagnostic and prognostic models are typically evaluated with measures of
accuracy that do not address clinical consequences. Decision-analytic techniques allow …

Background Automatic variable selection methods are usually discouraged in medical
research although we believe they might be valuable for studies where subject matter …

Partial expected value of perfect information (EVPI) calculations can quantify the value of
learning about particular subsets of uncertain parameters in decision models. Published …

The objective of this paper is to illustrate the advantages of the Bayesian approach in
quantifying, presenting, and reporting scientific evidence and in assisting decision making …

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 …

Most cost-effectiveness analyses consist of a baseline model that represents the absolute
natural history under a standard treatment in a comparator set and a model for relative …