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What is the Impact of the Analysis Method Used for Health State Utility Values on QALYs in Oncology? A Simulation Study Comparing Progression-Based and Time-to-Death Approaches

Author

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  • Anthony J. Hatswell

    (Delta Hat
    University College London)

  • Ash Bullement

    (Delta Hat)

  • Michael Schlichting

    (Merck KGaA)

  • Murtuza Bharmal

    (EMD Serono, Inc. (an affiliate of Merck KGaA, Darmstadt, Germany))

Abstract

Background Health state utility values (‘utilities’) are an integral part of health technology assessment. Though traditionally categorised by disease status in oncology (i.e. progression), several recent assessments have adopted values calculated according to the time that measures were recorded before death. We conducted a simulation study to understand the limitations of each approach, with a focus on mismatches between the way utilities are generated, and analysed. Methods Survival times were simulated based on published literature, with permutations of three utility generation mechanisms (UGMs) and utility analysis methods (UAMs): (1) progression based, (2) time-to-death based, and (3) a ‘combination approach’. For each analysis quality-adjusted life-years (QALYs) were estimated. Goodness of fit was assessed via percentage mean error (%ME) and mean absolute error (%MAE). Scenario analyses were performed varying individual parameters, with complex scenarios mimicking published studies. The statistical code is provided for transparency and to aid future work in the area. Results %ME and %MAE were lowest when the correct analysis form was specified (i.e. UGM and UAM aligned). Underestimates were produced when a time-to-death element was present in the UGM but not included in the UAM, while the ‘combined’ UAM produced overestimates irrespective of the UGM. Scenario analysis demonstrated the importance of the volume of available data beyond the initial time period, for example follow-up. Conclusions We show that the use of an incorrectly or over-specified UAM can result in substantial bias in the estimation of utilities. We present a flowchart to highlight the issues that may be faced.

Suggested Citation

  • Anthony J. Hatswell & Ash Bullement & Michael Schlichting & Murtuza Bharmal, 2021. "What is the Impact of the Analysis Method Used for Health State Utility Values on QALYs in Oncology? A Simulation Study Comparing Progression-Based and Time-to-Death Approaches," Applied Health Economics and Health Policy, Springer, vol. 19(3), pages 389-401, May.
  • Handle: RePEc:spr:aphecp:v:19:y:2021:i:3:d:10.1007_s40258-020-00620-6
    DOI: 10.1007/s40258-020-00620-6
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    References listed on IDEAS

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    1. Baptiste Leurent & Manuel Gomes & Rita Faria & Stephen Morris & Richard Grieve & James R. Carpenter, 2018. "Sensitivity Analysis for Not-at-Random Missing Data in Trial-Based Cost-Effectiveness Analysis: A Tutorial," PharmacoEconomics, Springer, vol. 36(8), pages 889-901, August.
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