Utility values in health technology assessments: a statistician's perspective

Abstract

This paper provides an introduction to utilities for statisticians working mainly in clinical research who have not had experience of health technology assessment work. Utility is the numeric valuation applied to a health state based on the preference of being in that state relative to perfect health. Utilities are often combined with survival data in health economic modelling to obtain quality-adjusted life years. There are several methods available for deriving the preference weights and the health states to which they are applied, and combining them to estimate utilities, and the clinical statistician has valuable skills that can be applied in ensuring the robustness of the trial design, data collection and analyses to obtain and handle this data. In addition to raising awareness of the subject and providing source references, the paper outlines the concepts and approaches around utilities using examples, discusses some of the key issues, and proposes areas where statisticians can collaborate with health economic colleagues to improve the quality of this important element of health technology assessment.

Factors influencing the statistical planning, design, conduct, analysis and reporting of trials in health care: A systematic review

“…[ 25 ] regarding “ study statisticians ” and “reporting statisticians”. [ 26 ] report the involvement of “medical statisticians” [ 27 ], describe “project statisticians”, while [ 28 ] mentions “ clinical statisticians ” but provide no definition. Statisticians may also be “ co-investigators ” [ 4 , 29 ] or “ consultants” [ 30 ] and such roles and responsibilities should be outlined [ 29 ].…”

“…A recurring factor reported to influence the quality of a trial positively was that statisticians should be involved from the beginning and in every stage through to the reporting [ 25 , 28 , 30 , 33 , 42 , [47] , [48] , [49] , [50] ]; Meeks et al, 2018). Recognising the importance of statistical expertise and early collaboration between statisticians, investigators and the clinical data management team was a key finding identified across several articles [ 26 , 40 ]; Crewson and Applegate., 2001 [ 29 , 35 , [51] , [52] , [53] , [54] ]; Meeks et al, 2018; [ 55 ].…”

Evidence‐Based Medicine in Otolaryngology Part 10: Cost‐Effectiveness Analyses in Otolaryngology

“…A second approach is the time trade-off technique; here, respondents quantify how time in a diseased state compares to time in an optimal CEA, cost-effectiveness analysis CUA, cost-utility analysis ICER, incremental cost-effectiveness ratio LY, Life year NHB, net health benefit NMB, net monetary benefit QALY, quality-adjusted life year WTP, willingness to pay health state. 2,39,[41][42][43][44][45] These decisional utility assessments have clear benefits, but critics of preference-based approaches note the potential for error when respondents attempt to forecast the value of a hypothetical, future health state. 46 These potential errors may be magnified in states that are sensitive to temporal preferences.…”

Evidence‐Based Medicine in Otolaryngology Part 9: Valuing Health Outcomes

“…Similarly, preference weights can be elicited from surveys of experts, such as health care professionals. 22…”