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Exclusion Criteria as Measurements II: Effects on Utility Functions

Author

Listed:
  • Barry Dewitt

    (Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA)

  • Baruch Fischhoff

    (Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA
    Institute for Politics and Strategy, Carnegie Mellon University, Pittsburgh, PA, USA)

  • Alexander L. Davis

    (Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA)

  • Stephen B. Broomell

    (Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA, USA)

  • Mark S. Roberts

    (Division of General Internal Medicine, University of Pittsburgh, Pittsburgh, PA, USA
    Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA)

  • Janel Hanmer

    (Division of General Internal Medicine, University of Pittsburgh, Pittsburgh, PA, USA)

Abstract

Background. Researchers often justify excluding some responses in studies eliciting valuations of health states as not representing respondents’ true preferences. Here, we examine the effects of applying 8 common exclusion criteria on societal utility estimates. Setting. An online survey of a US nationally representative sample ( N = 1164) used the standard gamble method to elicit preferences for health states defined by 7 health domains from the Patient-Reported Outcomes Measurement Information System (PROMIS ® ). Methods. We estimate the impacts of applying 8 commonly used exclusion criteria on mean utility values for each domain, using beta regression, a form of analysis suited to double-bounded scales, such as utility. Results. Exclusion criteria have varied effects on the utility functions for the different PROMIS health domains. As a result, applying those criteria would have varied effects on the value of treatments (and side effects) that change health status on those domains. Limitations. Although our method could be applied to any health utility judgments, the present estimates reflect the features of the study that produced them. Those features include the selected health domains, standard gamble method, and an online format that excluded some groups (e.g., visually impaired and illiterate individuals). We also examined only a subset of all possible exclusion criteria, selected to represent the space of possibilities, as characterized in a companion article. Conclusions. Exclusion criteria can affect estimates of the societal utility of health states. We use those effects, in conjunction with the results of the companion article, to make suggestions for selecting exclusion criteria in future studies.

Suggested Citation

  • Barry Dewitt & Baruch Fischhoff & Alexander L. Davis & Stephen B. Broomell & Mark S. Roberts & Janel Hanmer, 2019. "Exclusion Criteria as Measurements II: Effects on Utility Functions," Medical Decision Making, , vol. 39(6), pages 704-716, August.
    • Handle: RePEc:sae:medema:v:39:y:2019:i:6:p:704-716
    DOI: 10.1177/0272989X19862542
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    References listed on IDEAS

    as
    1. Barry Dewitt & Baruch Fischhoff & Alexander L. Davis & Stephen B. Broomell & Mark S. Roberts & Janel Hanmer, 2019. "Exclusion Criteria as Measurements I: Identifying Invalid Responses," Medical Decision Making, , vol. 39(6), pages 693-703, August.
    2. W Furlong & D Feeny & G Torrance & C Goldsmith & S DePauw & Z Zhu & M Denton & M Boyle, 1998. "Multiplicative Multi-Attribute Utility Function for the Health Utilities Index Mark 3 (HUI3) System: A Technical Report," Centre for Health Economics and Policy Analysis Working Paper Series 1998-11, Centre for Health Economics and Policy Analysis (CHEPA), McMaster University, Hamilton, Canada.
    3. Nancy J. Devlin & Paul Hansen & Paul Kind & Alan Williams, 2003. "Logical inconsistencies in survey respondents' health state valuations ‐ a methodological challenge for estimating social tariffs," Health Economics, John Wiley & Sons, Ltd., vol. 12(7), pages 529-544, July.
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