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Exclusion Criteria as Measurements II: Effects on Utility Functions.

Authors
  • Dewitt, Barry1
  • Fischhoff, Baruch1, 2
  • Davis, Alexander L1
  • Broomell, Stephen B3
  • Roberts, Mark S4, 5
  • Hanmer, Janel4
  • 1 Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA.
  • 2 Institute for Politics and Strategy, Carnegie Mellon University, Pittsburgh, PA, USA.
  • 3 Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.
  • 4 Division of General Internal Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
  • 5 Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
Type
Published Article
Journal
Medical decision making : an international journal of the Society for Medical Decision Making
Publication Date
Aug 01, 2019
Volume
39
Issue
6
Pages
704–716
Identifiers
DOI: 10.1177/0272989X19862542
PMID: 31462183
Source
Medline
Keywords
Language
English
License
Unknown

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.

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