Estimating Health-State Utility for Economic Models in Clinical Studies: An ISPOR Good Research Practices Task Force Report

Wolowacz, Sorrel; Briggs, Andrew; Belozeroff, Vasily; Clarke, Philip; Doward, Lynda; Goeree, Ron; Lloyd, Andrew; Norman, Richard

doi:10.1016/j.jval.2016.06.001

Cited by 122 publications

(140 citation statements)

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“…Thus, mapping is an issue both for economic evaluation alongside trial data analysis without PBMs as well as for many economic modeling studies. And because studies that have been conducted historically will remain part of the evidence base as comparators for the evaluation of new technologies, mapping is likely to remain a requirement for some time, even when good practices for utility estimation are followed in contemporary clinical studies 12 .…”

Section: Introductionmentioning

confidence: 99%

Mapping to Estimate Health-State Utility from Non–Preference-Based Outcome Measures: An ISPOR Good Practices for Outcomes Research Task Force Report

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ACKNOWLEDGMENTSThe individual contributions of Sorrel Wolowacz and Richard Willke are gratefully acknowledged. We thank all reviewers who commented during our forums at the ISPOR Milan and Amsterdam European Congresses. We especially thank the following individuals who reviewed drafts of the report and submitted written comments. Their feedback has both improved the manuscript and made it an expert consensus ISPOR task force report. Many thanks to Tony Ades, René Allard, Gouri Shankar Bhattacharyya, Lance Brannman, Michael Carter, David Cella, Akriti Chahar, Stephanie Yanjing Chen, Shiva Devarakonda, Salah Ghabri, Linda Gore Martin, Inigo Gorostiza, Thomas Grochtdreis, Michael Hagan, Nadine Hillock, Cynthia Holmes, Shrividya Iyer, Steve Kay, Jeanette Kusel, Ramanath KV, Dawn Lee, Joanna Le niowska, Sophia E. Marsh, Alan Martin, Nicholas Mitsakakis, Sharanya Murty, Nneka Onwudiwe, Guilhem Pietri, G.M. Rabiul Islam, Ghabri Salah, Mihail Samnaliev, Carsten Schousboe, Sarah Shingler, Fatema Turkistani, and Uday Venkat. Finally, many thanks to Theresa Tesoro for her assistance in developing this task force report. 2 AbstractEconomic evaluation conducted in terms of cost per Quality Adjusted Life Year (QALY) provides information that decision makers find useful in many parts of the world. Ideally, clinical studies designed to assess the effectiveness of health technologies would include outcome measures that are directly linked to health utility in order to calculate QALYs. Often this does not happen and, even where it does, clinical studies may be insufficient for a cost-utility assessment. Mapping can solve this problem. It uses an additional dataset to estimate the relationship between outcomes measured in clinical studies and health utility. This bridges the evidence gap between available evidence on the effect of a health technology in one metric and the requirement for decision makers to express it in a different one (QALYs). In 2014, ISPOR established a Good Practices for Outcome Research Task Force for mapping studies. This Task Force Report provides recommendations to analysts undertaking mapping studies, those that use the results in cost utility analysis, and those that need to critically review such studies. The recommendations cover all areas of mapping practice: the selection of datasets for the mapping estimation, model selection and performance assessment, reporting standards, and the use of results including the appropriate reflection of variability and uncertainty. This report is unique because it takes an international perspective, is comprehensive in its coverage of the aspects of mapping practice, and reflects the current state of the art.

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Section: Introductionmentioning

confidence: 99%

Mapping to Estimate Health-State Utility from Non–Preference-Based Outcome Measures: An ISPOR Good Practices for Outcomes Research Task Force Report

et al. 2017

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Section: Recommendations For Health State Utility Valuesmentioning

confidence: 99%

“…More recently, their use in DAMs has formed the focus of two International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Task Force reports [7,8]. The first describes a framework for collecting HSUVs within clinical studies and covers a broad spectrum from early planning of requirements, inclusion in clinical trial protocols, the advantages and design of alternative sources such as prospective and cross-sectional observational studies, statistical analyses and reporting standards [7]. The second provides recommendations relating to conducting highquality mapping studies involving HSUVs and includes good practices for the selection of datasets used to obtain the mapping function, model selection and performance assessment, the use of results (including characterising variability and uncertainty appropriately), and reporting standards [8].…”

Section: Recommendations For Health State Utility Valuesmentioning

confidence: 99%

“…In addition to identifying an appropriate preference-based measure for the particular condition (Fig. 1), and satisfying any associated reimbursement agency requirements, factors such as the advantages (or disadvantages) of collecting utility evidence in randomised clinical trials and other sources [7], the potential need to use a mapping function to predict the required HSUVs [8], and satisfying the exact and often evolving health states used within the DAM all require consideration.Once the most appropriate HSUVs available have been identified, these may not match the requirements of the DAM exactly and analysts may then need to make a series of methodological decisions or assumptions related to the practicalities of using this evidence within the DAM. These may include adjustments to account for age, sex or adverse events, evidence for the baseline or counterfactual (i.e.…”

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Sourcing and Using Appropriate Health State Utility Values in Economic Models in Health Care

et al. 2017

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Decision analytic models (DAMs) in health care are generally used to assess different interventions to determine which provides the best value for money [1]. These models explore both the costs and benefits accrued by patients receiving the interventions, and results are presented in terms of the incremental cost per benefit of the intervention under evaluation [2]. While not all policy decision makers and reimbursement authorities conform to the same methodology [3], there has been a substantial growth in the use of incremental cost per quality-adjusted life-year (QALY) over the last two decades [4]. As the incremental QALY is the denominator in the outputs of the model, results can be sensitive to both the health state utility values (HSUVs) used and the methods and techniques used to deploy these values within the model [5].The different components or stages involved in sourcing appropriate HSUVs (Fig. 1) are rarely independent and the process is generally iterative and often challenging [6]. In addition to identifying an appropriate preference-based measure for the particular condition (Fig. 1), and satisfying any associated reimbursement agency requirements, factors such as the advantages (or disadvantages) of collecting utility evidence in randomised clinical trials and other sources [7], the potential need to use a mapping function to predict the required HSUVs [8], and satisfying the exact and often evolving health states used within the DAM all require consideration.Once the most appropriate HSUVs available have been identified, these may not match the requirements of the DAM exactly and analysts may then need to make a series of methodological decisions or assumptions related to the practicalities of using this evidence within the DAM. These may include adjustments to account for age, sex or adverse events, evidence for the baseline or counterfactual (i.e. the trajectory of HSUVs for people who do not have a particular health condition or clinical event), estimate HSUVs for comorbidities (when a second health condition is present concurrently with the primary health condition), and characterising uncertainty appropriately (when using summary statistics sourced from different studies) (Fig. 1). Existing Recommendations on Best Practice Recommendations for Decision Analytic ModelsGuidance on appropriate DAM approaches are available in the literature [1, 2, 9-11] and cover the choice of model structure [12]; measuring and estimating costs [13,14]; identifying, reviewing and synthesising evidence on healthrelated quality of life [6,15]; and methods for characterising uncertainty [16]. In addition, a number of checklists have been published over the years to encourage good practice in this area [1,2,9,17,18].

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Explaining Health State Utility Assessment

Chang

Saigal

Raldow

2020

JAMA

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Estimating Health-State Utility for Economic Models in Clinical Studies: An ISPOR Good Research Practices Task Force Report

Cited by 122 publications

References 50 publications

Mapping to Estimate Health-State Utility from Non–Preference-Based Outcome Measures: An ISPOR Good Practices for Outcomes Research Task Force Report

Mapping to Estimate Health-State Utility from Non–Preference-Based Outcome Measures: An ISPOR Good Practices for Outcomes Research Task Force Report

Sourcing and Using Appropriate Health State Utility Values in Economic Models in Health Care

Explaining Health State Utility Assessment

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