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Exclusion Criteria as Measurements I: Identifying Invalid Responses

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
    The 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. In a systematic review, Engel et al. found large variation in the exclusion criteria used to remove responses held not to represent genuine preferences in health state valuation studies. We offer an empirical approach to characterizing the similarities and differences among such criteria. Setting. Our analyses use data from an online survey that elicited preferences for health states defined by domains from the Patient-Reported Outcomes Measurement Information System (PROMIS ® ), with a U.S. nationally representative sample ( N = 1164). Methods. We use multidimensional scaling to investigate how 10 commonly used exclusion criteria classify participants and their responses. Results. We find that the effects of exclusion criteria do not always match the reasons advanced for applying them. For example, excluding very high and very low values has been justified as removing aberrant responses. However, people who give very high and very low values prove to be systematically different in ways suggesting that such responses may reflect different processes. Conclusions. Exclusion criteria intended to remove low-quality responses from health state valuation studies may actually remove deliberate but unusual ones. A companion article examines the effects of the exclusion criteria on societal utility estimates.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:medema:v:39:y:2019:i:6:p:693-703
    DOI: 10.1177/0272989X19856617
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    References listed on IDEAS

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    1. J. Gower & P. Legendre, 1986. "Metric and Euclidean properties of dissimilarity coefficients," Journal of Classification, Springer;The Classification Society, vol. 3(1), pages 5-48, March.
    2. Julie Ratcliffe & John Brazier & Aki Tsuchiya & Tara Symonds & Martin Brown, 2009. "Using DCE and ranking data to estimate cardinal values for health states for deriving a preference‐based single index from the sexual quality of life questionnaire," Health Economics, John Wiley & Sons, Ltd., vol. 18(11), pages 1261-1276, November.
    3. Fischhoff, Baruch & Kadvany, John, 2011. "Risk: A Very Short Introduction," OUP Catalogue, Oxford University Press, number 9780199576203.
    4. 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.
    5. Versteegh, M.M. & Brouwer, W.B.F., 2016. "Patient and general public preferences for health states: A call to reconsider current guidelines," Social Science & Medicine, Elsevier, vol. 165(C), pages 66-74.
    6. Jan Van den Broeck & Solveig Argeseanu Cunningham & Roger Eeckels & Kobus Herbst, 2005. "Data Cleaning: Detecting, Diagnosing, and Editing Data Abnormalities," PLOS Medicine, Public Library of Science, vol. 2(10), pages 1-1, September.
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    1. 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.

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