Background: Economic evaluations of health interventions pose a particular challenge for reporting because substantial information must be conveyed to allow scrutiny of study findings. Despite a growth in published reports, existing reporting guidelines are not widely adopted. There is also a need to consolidate and update existing guidelines and promote their use in a user-friendly manner. A checklist is one way to help authors, editors, and peer reviewers use guidelines to improve reporting. Objective: The task force's overall goal was to provide recommendations to optimize the reporting of health economic evaluations. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement is an attempt to consolidate and update previous health economic evaluation guidelines into one current, useful reporting guidance. The CHEERS Elaboration and Explanation Report of the ISPOR Health Economic Evaluation Publication Guidelines Good Reporting Practices Task Force facilitates the use of the CHEERS statement by providing examples and explanations for each recommendation. The primary audiences for the CHEERS statement are researchers reporting economic evaluations and the editors and peer reviewers assessing them for publication. Methods: The need for new reporting guidance was identified by a survey of medical editors. Previously published checklists or guidance documents related to reporting economic evaluations were identified from a systematic review and subsequent survey of task force members. A list of possible items from these efforts was created. A two-round, modified Delphi Panel with representatives from academia, clinical practice, industry, and government, as well as the editorial community, was used to identify a minimum set of items important for reporting from the larger list. Results: Out of 44 candidate items, 24 items and accompanying recommendations were developed, with some specific recommendations for single study-based and model-based economic evaluations. The final recommendations are subdivided into six main categories: 1) title and abstract, 2) introduction, 3) methods, 4) results, 5) discussion, and 6) other. The recommendations are contained in the CHEERS statement, a user-friendly 24-item checklist. The task force report provides explanation and elaboration, as well as an example for each recommendation.

A model's purpose is to inform medical decisions and health care resource allocation. Modelers employ quantitative methods to structure the clinical, epidemiological, and economic evidence base and gain qualitative insight to assist decision makers in making better decisions. From a policy perspective, the value of a model-based analysis lies not simply in its ability to generate a precise point estimate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty surrounding this outcome and the ultimate decision being addressed. Different concepts relating to uncertainty in decision modeling are explored. Stochastic (first-order) uncertainty is distinguished from both parameter (second-order) uncertainty and from heterogeneity, with structural uncertainty relating to the model itself forming another level of uncertainty to consider. The article argues that the estimation of point estimates and uncertainty in parameters is part of a single process and explores the link between parameter uncertainty through to decision uncertainty and the relationship to value of information analysis. The article also makes extensive recommendations around the reporting of uncertainty, in terms of both deterministic sensitivity analysis techniques and probabilistic methods. Expected value of perfect information is argued to be the most appropriate presentational technique, alongside cost-effectiveness acceptability curves, for representing decision uncertainty from probabilistic analysis.

Models--mathematical frameworks that facilitate estimation of the consequences of health care decisions--have become essential tools for health technology assessment. Evolution of the methods since the first ISPOR Modeling Task Force reported in 2003 has led to a new Task Force, jointly convened with the Society for Medical Decision Making, and this series of seven articles presents the updated recommendations for best practices in conceptualizing models; implementing state-transition approaches, discrete event simulations, or dynamic transmission models; and dealing with uncertainty and validating and reporting models transparently. This overview article introduces the work of the Task Force, provides all the recommendations, and discusses some quandaries that require further elucidation. The audience for these articles includes those who build models, stakeholders who utilize their results, and, indeed, anyone concerned with the use of models to support decision making.

Aims/hypothesis. The aim of this study was to develop a simulation model for Type 2 diabetes that can be used to estimate the likely occurrence of major diabetes-related complications over a lifetime, in order to calculate health economic outcomes such as qualityadjusted life expectancy. Methods. Equations for forecasting the occurrence of seven diabetes-related complications and death were estimated using data on 3642 patients from the United Kingdom Prospective Diabetes Study (UKPDS). After examining the internal validity, the UKPDS Outcomes Model was used to simulate the mean difference in expected quality-adjusted life years between the UKPDS regimens of intensive and conventional blood glucose control. Results. The model's forecasts fell within the 95% confidence interval for the occurrence of observed events during the UKPDS follow-up period. When the model was used to simulate event history over patients' lifetimes, those treated with a regimen of conventional glucose control could expect 16.35 undiscounted quality-adjusted life years, and those receiving treatment with intensive glucose control could expect 16.62 quality-adjusted life years, a difference of 0.27 (95% CI: −0.48 to 1.03). Conclusions/interpretations. The UKPDS OutcomesModel is able to simulate event histories that closely match observed outcomes in the UKPDS and that can be extrapolated over patients' lifetimes. Its validity in estimating outcomes in other groups of patients, however, remains to be evaluated. The model allows simulation of a range of long-term outcomes, which should assist in informing future economic evaluations of interventions in Type 2 diabetes. The centres of the UKPDS are listed at the end of the paper Conflict of interest. Several authors (as indicated above) are employed by the University of Oxford. This paper describes and places in the public domain a simulation model that we have called the UKPDS Outcomes Model. All of the information necessary to reproduce the UKPDS Outcomes Model is provided in this article, but it is conceivable that a future user with a commercial interest in the UKPDS Outcomes Model might prefer to use the software already created by University programmers. Depending on the nature of the proposed use of the UKPDS Outcomes Model, the University of Oxford might charge a fee in this case.

Cost‐effectiveness acceptability curves (CEACs) have been widely adopted as a method to quantify and graphically represent uncertainty in economic evaluation studies of health‐care technologies. However, there remain some common fallacies regarding the nature and shape of CEACs that largely result from the ‘textbook’ illustration of the CEAC. This ‘textbook’ CEAC shows a smooth curve starting at probability 0, with an asymptote to 1 for higher money values of the health outcome (λ). But this familiar ‘ogive’ shape which makes the ‘textbook’ CEAC look like a cumulative distribution function is just one special case of the CEAC. The reality is that the CEAC can take many shapes and turns because it is a graphic transformation from the cost‐effectiveness plane, where the joint density of incremental costs and effects may ‘straddle’ quadrants with attendant discontinuities and asymptotes. In fact CEACs: (i) do not have to cut the y‐axis at 0; (ii) do not have to asymptote to 1; (iii) are not always monotonically increasing in λ; and (iv) do not represent cumulative distribution functions (cdfs). Within this paper we present a ‘gallery’ of CEACs in order to identify the fallacies and illustrate the facts surrounding the CEAC. The aim of the paper is to serve as a reference tool to accompany the increased use of CEACs within major medical journals. Copyright © 2004 John Wiley & Sons, Ltd.