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  3. Developing Best Practice For Analysing Individual Patient Utility Data In Health Technology Assessment: A Modified Delphi Study.
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  3. Developing Best Practice For Analysing Individual Patient Utility Data In Health Technology Assessment: A Modified Delphi Study.

Related Experiment Video

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
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Published on: January 12, 2018

Developing Best Practice for Analysing Individual Patient Utility Data in Health Technology Assessment: A Modified

Frank Grimsey Jones1, Alan Lovell1, Saul Stevens1

  • 1Peninsula Technology Assessment Group (PenTAG), University of Exeter Medical School, Exeter.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|June 23, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study developed best practice guidance for analyzing individual patient utility data, crucial for health economic models. Consensus was reached on methods, adjustments, and sensitivity analyses, though evidence gaps remain.

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14:43

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Published on: January 12, 2018

Area of Science:

  • Health Economics
  • Biostatistics
  • Health Technology Assessment

Background:

  • Accurate health utilities are essential for health economic models and calculating quality-adjusted life-years (QALYs).
  • Individual patient utility data are vital for deriving health state utility values.
  • No established best practice guidance exists for analyzing patient-level utility data.

Purpose of the Study:

  • To develop best practice guidance for analyzing individual patient utility data.
  • To identify gaps in the current literature and analytical methods.
  • To inform health technology assessment agencies for consistent data analysis.

Main Methods:

  • A scoping survey, literature reviews, and NICE technology appraisal reviews were conducted.
  • A modified Delphi process with 31 participants (health economists, statisticians) generated consensus.
  • Key analysis considerations included utility distributions, data characteristics, and analytical methods.

    • Main Results:

    • Consensus was achieved on 13 topics across three analysis stages: core methods, adjustments, and sensitivity analyses.
    • The Delphi panel refined existing recommendations, adding clarifications and expansions.
    • Broad consensus was reached by the end of Round 3, with refined wording and expanded recommendations.

    Conclusions:

    • Best practice guidance for analyzing individual patient utility data has been informed by this research.
    • Significant evidence gaps persist, particularly concerning optimal methods for handling specific data characteristics.
    • The findings will enable clearer, more consistent instructions for robust and transparent utility data analysis by HTA agencies.