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

Economic 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 to calculate QALYs. Often this does not happen, and even when it does, clinical studies may be insufficient for a cost-utility assessment. Mapping can solve this problem. It uses an additional data set 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 data sets 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.

[1]  J. S. Cramer,et al.  Econometric Applications of Maximum Likelihood Methods , 1986 .

[2]  S. Davis,et al.  Mapping from the Health Assessment Questionnaire to the EQ-5D: the impact of different algorithms on cost-effectiveness results. , 2014, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[3]  Albert W Wu,et al.  Addressing ceiling effects in health status measures: a comparison of techniques applied to measures for people with HIV disease. , 2007, Health services research.

[4]  S. Brophy,et al.  Modeling Health State Utility Values in Ankylosing Spondylitis: Comparisons of Direct and Indirect Methods. , 2015, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[5]  Allan Wailoo,et al.  A Comparison of Direct and Indirect Methods for the Estimation of Health Utilities from Clinical Outcomes , 2014, Medical decision making : an international journal of the Society for Medical Decision Making.

[6]  K. Abrams,et al.  Good Practice Guidelines for the use of Statistical Regression Models in Economic Evaluations , 2013, PharmacoEconomics.

[7]  Andrew Briggs,et al.  Estimating Health-State Utility for Economic Models in Clinical Studies: An ISPOR Good Research Practices Task Force Report. , 2016, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[8]  Oliver Rivero-Arias,et al.  Estimating the Association between SF-12 Responses and EQ-5D Utility Values by Response Mapping , 2006, Medical decision making : an international journal of the Society for Medical Decision Making.

[9]  Andrew Briggs,et al.  Mapping the QLQ-C30 quality of life cancer questionnaire to EQ-5D patient preferences , 2010, The European Journal of Health Economics.

[10]  Roberta Ara,et al.  Tails from the peak district: adjusted limited dependent variable mixture models of EQ-5D questionnaire health state utility values. , 2012, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[11]  A. Wailoo,et al.  Modelling the relationship between the WOMAC osteoarthritis index and EQ-5D , 2013, Health and Quality of Life Outcomes.

[12]  Julia Earnshaw,et al.  NICE Guide to the Methods of Technology Appraisal , 2012, PharmacoEconomics.

[13]  J. Brazier,et al.  Use of generic and condition-specific measures of health-related quality of life in NICE decision-making: a systematic review, statistical modelling and survey. , 2014, Health technology assessment.

[14]  Andrea Manca,et al.  Prediction of patient‐reported outcome measures via multivariate ordered probit models , 2015 .

[15]  Mark Oppe,et al.  Preferred reporting items for studies mapping onto preference-based outcome measures: The MAPS statement. , 2015, Journal of medical economics.

[16]  David Parkin,et al.  Using the EQ-5D as a performance measurement tool in the NHS , 2009 .

[17]  John Brazier,et al.  The Use of Health State Utility Values in Decision Models , 2017, PharmacoEconomics.

[18]  D. Feeny,et al.  The Health Utilities Index (HUI®): concepts, measurement properties and applications , 2003, Health and quality of life outcomes.

[19]  R. Goeree,et al.  Analysis of health utility data when some subjects attain the upper bound of 1: are Tobit and CLAD models appropriate? , 2010, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[20]  Sarah E Lamb,et al.  Mapping between the Roland Morris Questionnaire and generic preference-based measures. , 2014, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[21]  Aki Tsuchiya,et al.  Using the health assessment questionnaire to estimate preference-based single indices in patients with rheumatoid arthritis. , 2007, Arthritis and rheumatism.

[22]  Nicky J Welton,et al.  NICE DSU Technical Support Document 18 , 2011 .

[23]  M. Weinstein,et al.  Foundations of cost-effectiveness analysis for health and medical practices. , 1977, The New England journal of medicine.

[24]  Jerry G. Thursby,et al.  Econometric Applications of Maximum Likelihood Methods. , 1988 .

[25]  A. Brennan,et al.  The Sheffield rheumatoid arthritis health economic model. , 2011, Rheumatology.

[26]  A. Williams EuroQol : a new facility for the measurement of health-related quality of life , 1990 .

[27]  S. Acaster,et al.  Mapping the EQ-5D index from the cystic fibrosis questionnaire-revised using multiple modelling approaches , 2015, Health and Quality of Life Outcomes.

[28]  J. Brazier,et al.  The Estimation of a Preference-Based Measure of Health From the SF-12 , 2004, Medical care.

[29]  J. Brazier,et al.  Deriving preference-based single indices from non-preference based condition-specific instruments: converting AQLQ into EQ5D indices , 2002 .

[30]  Andrew Gelman,et al.  Inference from Simulations and Monitoring Convergence , 2011 .

[31]  Matthias Hunger,et al.  Analysis of SF-6D index data: is beta regression appropriate? , 2011, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[32]  Andrea Manca,et al.  Regression Estimators for Generic Health-Related Quality of Life and Quality-Adjusted Life Years , 2012, Medical decision making : an international journal of the Society for Medical Decision Making.

[33]  Anna García-Altés,et al.  Spanish recommendations on economic evaluation of health technologies , 2010, The European Journal of Health Economics.

[34]  Aki Tsuchiya,et al.  A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures , 2010, The European Journal of Health Economics.