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Developing Best Practice for Analyzing 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, England, UK.
Summary
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.
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.
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