Objective:Objective: The aim of this study was to examine how calibration uncertainty affects the overall uncertainty of a mathematical model and to evaluate potential drivers of calibration uncertainty. Abstract: Methods:Methods: A lifetime Markov model of the natural history of human...

This paper presents a simple model to guide the efficient allocation of resources in community programs to detect and treat hypertension. We model such programs as n-component series systems, where each stage must function in sequence in order for the system as a whole to operate. The model...

Decisions in health care must be made, despite uncertainty about benefits, risks, and costs. Value of information analysis is a theoretically sound method to estimate the expected value of future quantitative research pertaining to an uncertain decision. If the expected value of future research...

Are we getting the most health improvement possible for our money. In other words, are all the things that we do in medicine really worth it? That is where cost-effectiveness comes in. As a nation, we have been unwilling, at least publicly, to look explicitly at the value, in terms of improved...

Background: Mathematical models are commonly used to predict future benefits of new therapies or interventions in the healthcare setting. The reliability of model results is greatly dependent on accuracy of model inputs but on occasion, data sources may not provide all the required inputs....

This comprehensive collection brings together more than 50 contributions from some of the most influential researchers in health economics. It authoritatively covers theoretical and empirical issues in health economics, with a balanced range of material on equity and efficiency in health care...