Increasingly complex models are being used to evaluate the cost‐effectiveness of medical
interventions. We describe the multiple sources of uncertainty that are relevant to such …

Over the last decade or so, there have been many developments in methods to handle
uncertainty in cost‐effectiveness studies. In decision modelling, it is widely accepted that …

There was a time when a simple dichotomy characterized many health economic
evaluations. On the one hand there were those economic appraisals that were conducted …

Decision analytic models used for health technology assessment are subject to
uncertainties. These uncertainties can be quantified probabilistically, by placing distributions …

BACKGROUND AND OBJECTIVE: Although randomized controlled trials (RCTs) are
conducted to establish whether novel interventions work on average in the patient …

Health economic decision models are subject to various forms of uncertainty, including
uncertainty about the parameters of the model and about the model structure. These …

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 …

Parameter uncertainty, patient heterogeneity, and stochastic uncertainty of outcomes are
increasingly important concepts in medical decision models. The purpose of this study is to …

When multiple parameters are estimated from the same synthesis model, it is likely that
correlations will be induced between them. Network meta-analysis (mixed treatment …

The use of modelling in economic evaluation is widespread, and it most often involves
synthesising data from a number of sources. However, even when economic evaluations are …