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Exclusion Criteria in National Health State Valuation Studies

Author

Listed:
  • Lidia Engel
  • Nick Bansback
  • Stirling Bryan
  • Mary M. Doyle-Waters
  • David G. T. Whitehurst

Abstract

Background. Health state valuation data are often excluded from studies that aim to provide a nationally representative set of values for preference-based health-related quality of life (HRQoL) instruments. The purpose was to provide a systematic examination of exclusion criteria used in the derivation of societal scoring algorithms for preference-based HRQoL instruments. Methods. Data sources included MEDLINE, official instrument websites, and publication reference lists. Analyses that used data from national valuation studies and reported a scoring algorithm for a generic preference-based HRQoL instrument were included. Data extraction included exclusion criteria and associated justifications, exclusion rates, the characteristics of excluded respondents, and analyses that explored consequential implications of exclusion criteria on the respective national tariff. Results. Seventy-six analyses (from 70 papers) met the inclusion criteria. In addition to being excluded for logical inconsistencies, respondents were often excluded if they valued fewer than 3 health states or if they gave the same value to all health states. Numerous other exclusion criteria were identified, with varying degrees of justification, often based on an assumption that respondents did not understand the task or as a consequence of the chosen statistical modeling techniques. Rates of exclusion ranged from 0% to 65%, with excluded respondents more likely to be older, less educated, and less healthy. Limitations included that the database search was confined to MEDLINE; study selection focused on national valuation studies that used standard gamble, time tradeoff, and/or visual analog scale techniques; and only English-language studies were included. Conclusion. Exclusion criteria used in national valuation studies vary considerably. Further consideration is necessary in this important and influential area of research, from the design stage to the reporting of results.

Suggested Citation

  • Lidia Engel & Nick Bansback & Stirling Bryan & Mary M. Doyle-Waters & David G. T. Whitehurst, 2016. "Exclusion Criteria in National Health State Valuation Studies," Medical Decision Making, , vol. 36(7), pages 798-810, October.
  • Handle: RePEc:sae:medema:v:36:y:2016:i:7:p:798-810
    DOI: 10.1177/0272989X15595365
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    References listed on IDEAS

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    2. Yan Feng & Nancy J. Devlin & Koonal K. Shah & Brendan Mulhern & Ben van Hout, 2018. "New methods for modelling EQ‐5D‐5L value sets: An application to English data," Health Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 23-38, January.
    3. Nancy J. Devlin & Koonal K. Shah & Yan Feng & Brendan Mulhern & Ben van Hout, 2018. "Valuing health‐related quality of life: An EQ‐5D‐5L value set for England," Health Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 7-22, January.
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    5. S. A. Lipman & V. T. Reckers-Droog & M. Karimi & M. Jakubczyk & A. E. Attema, 2021. "Self vs. other, child vs. adult. An experimental comparison of valuation perspectives for valuation of EQ-5D-Y-3L health states," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(9), pages 1507-1518, December.
    6. Marian Sorin Paveliu & Elena Olariu & Raluca Caplescu & Yemi Oluboyede & Ileana-Gabriela Niculescu-Aron & Simona Ernu & Luke Vale, 2021. "Estimating an EQ-5D-3L Value Set for Romania Using Time Trade-Off," IJERPH, MDPI, vol. 18(14), pages 1-16, July.
    7. Stefan A. Lipman, 2021. "Time for Tele-TTO? Lessons Learned From Digital Interviewer-Assisted Time Trade-Off Data Collection," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 14(5), pages 459-469, September.

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