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Is the whole larger than the sum of its parts? Impact of missing data imputation in economic evaluation conducted alongside randomized controlled trials

Author

Listed:
  • Bernhard Michalowsky

    (Site Rostock/Greifswald
    McMaster University
    Program for Health Economics and Outcome Measures (PHENOM))

  • Wolfgang Hoffmann

    (Site Rostock/Greifswald
    University Medicine Greifswald (UMG))

  • Kevin Kennedy

    (McMaster University
    Program for Health Economics and Outcome Measures (PHENOM))

  • Feng Xie

    (McMaster University
    Program for Health Economics and Outcome Measures (PHENOM)
    McMaster University)

Abstract

Outcomes in economic evaluations, such as health utilities and costs, are products of multiple variables, often requiring complete item responses to questionnaires. Therefore, missing data are very common in cost-effectiveness analyses. Multiple imputations (MI) are predominately recommended and could be made either for individual items or at the aggregate level. We, therefore, aimed to assess the precision of both MI approaches (the item imputation vs. aggregate imputation) on the cost-effectiveness results. The original data set came from a cluster-randomized, controlled trial and was used to describe the missing data pattern and compare the differences in the cost-effectiveness results between the two imputation approaches. A simulation study with different missing data scenarios generated based on a complete data set was used to assess the precision of both imputation approaches. For health utility and cost, patients more often had a partial (9% vs. 23%, respectively) rather than complete missing (4% vs. 0%). The imputation approaches differed in the cost-effectiveness results (the item imputation: − 61,079€/QALY vs. the aggregate imputation: 15,399€/QALY). Within the simulation study mean relative bias (

Suggested Citation

  • Bernhard Michalowsky & Wolfgang Hoffmann & Kevin Kennedy & Feng Xie, 2020. "Is the whole larger than the sum of its parts? Impact of missing data imputation in economic evaluation conducted alongside randomized controlled trials," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(5), pages 717-728, July.
  • Handle: RePEc:spr:eujhec:v:21:y:2020:i:5:d:10.1007_s10198-020-01166-z
    DOI: 10.1007/s10198-020-01166-z
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    References listed on IDEAS

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    1. Rita Faria & Manuel Gomes & David Epstein & Ian White, 2014. "A Guide to Handling Missing Data in Cost-Effectiveness Analysis Conducted Within Randomised Controlled Trials," PharmacoEconomics, Springer, vol. 32(12), pages 1157-1170, December.
    2. Bernhard Michalowsky & Steffen Flessa & Tilly Eichler & Johannes Hertel & Adina Dreier & Ina Zwingmann & Diana Wucherer & Henriette Rau & Jochen René Thyrian & Wolfgang Hoffmann, 2018. "Healthcare utilization and costs in primary care patients with dementia: baseline results of the DelpHi-trial," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(1), pages 87-102, January.
    3. Sian Marie Noble & William Hollingworth & Kate Tilling, 2012. "Missing data in trial‐based cost‐effectiveness analysis: the current state of play," Health Economics, John Wiley & Sons, Ltd., vol. 21(2), pages 187-200, February.
    4. David K. Blough & Scott Ramsey & Sean D. Sullivan & Roger Yusen, 2009. "The impact of using different imputation methods for missing quality of life scores on the estimation of the cost‐effectiveness of lung‐volume‐reduction surgery," Health Economics, John Wiley & Sons, Ltd., vol. 18(1), pages 91-101, January.
    5. Baptiste Leurent & Manuel Gomes & James R. Carpenter, 2018. "Missing data in trial‐based cost‐effectiveness analysis: An incomplete journey," Health Economics, John Wiley & Sons, Ltd., vol. 27(6), pages 1024-1040, June.
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Chris Sampson’s journal round-up for 27th July 2020
      by Chris Sampson in The Academic Health Economists' Blog on 2020-07-27 11:00:01

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    More about this item

    Keywords

    Missing data; Multiple imputation; Cost-effectiveness analysis; Cost–utility analysis;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • I1 - Health, Education, and Welfare - - Health
    • I10 - Health, Education, and Welfare - - Health - - - General

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