Evidence map›Paper›PMID 38421583›Full record

ArticleThe patient2024

Using Patient Preferences in Health Technology Assessment: Evaluating Quality-Adjusted Survival Equivalents (QASE) for the Quantification of Non-health Benefits.

Kevin Marsh, Hannah Collacott, Jim Thomson, Jonathan Mauer, Stephen Watt, Koonal Shah, Brett Hauber, Louis Garrison, Mendwas Dzingina

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Article in The patient, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Kevin MarshEvidera, 201 Talgarth Rd, London, W6 8BJ, UK. kevin.marsh@evidera.com.ORCID 0000-0002-7912-9485
Hannah CollacottEvidera, 201 Talgarth Rd, London, W6 8BJ, UK.
Jim ThomsonViiV Healthcare, London, UK.
Jonathan MauerPfizer, New York, NY, USA.
Stephen WattPfizer, New York, NY, USA.
Koonal ShahNational Institute for Health and Care Excellence, London, UK.
Brett HauberPfizer, New York, NY, USA.
Louis GarrisonThe Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, Department of Pharmacy, University of Washington, Seattle, WA, USA.
Mendwas DzinginaPfizer, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Interest in using patient preference (PP) data alongside traditional economic models in health technology assessment (HTA) is growing, including using PP data to quantify non-health benefits. However, this is limited by a lack of standardised methods. In this article, we describe a method for using discrete choice experiment (DCE) data to estimate the value of non-health benefits in terms of quality-adjusted survival equivalence (QASE), which is consistent with the concept of value prevalent among HTA agencies. We describe how PP data can be used to estimate QASE, assess the ability to test the face-validity of QASE estimates of changes in mode of administration calculated from five published DCE oncology studies and review the methodological and normative considerations associated with using QASE to support HTA. We conclude that QASE may have some methodological advantages over alternative methods, but this requires DCEs to estimate second-order effects between length and quality of life. In addition, empirical work has yet to be undertaken to substantiate this advantage and demonstrate the validity of QASE. Further work is also required to align QASE with normative objectives of HTA agencies. Estimating QASE would also have implications for the conduct of DCEs, including standardising and defining more clear attribute definitions.

Indexed as

Patient PreferenceQuality-Adjusted Life YearsTechnology Assessment, BiomedicalChoice BehaviorCost-Benefit AnalysisHumansQuality of Life

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.