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A Bayesian parametric approach to handle missing longitudinal outcome data in trial‐based health economic evaluations

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  • Andrea Gabrio
  • Michael J. Daniels
  • Gianluca Baio

Abstract

Trial‐based economic evaluations are typically performed on cross‐sectional variables, derived from the responses for only the completers in the study, using methods that ignore the complexities of utility and cost data (e.g. skewness and spikes). We present an alternative and more efficient Bayesian parametric approach to handle missing longitudinal outcomes in economic evaluations, while accounting for the complexities of the data. We specify a flexible parametric model for the observed data and partially identify the distribution of the missing data with partial identifying restrictions and sensitivity parameters. We explore alternative non‐ignorable missingness scenarios through different priors for the sensitivity parameters, calibrated on the observed data. Our approach is motivated by, and applied to, data from a trial assessing the cost‐effectiveness of a new treatment for intellectual disability and challenging behaviour.

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  • Andrea Gabrio & Michael J. Daniels & Gianluca Baio, 2020. "A Bayesian parametric approach to handle missing longitudinal outcome data in trial‐based health economic evaluations," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 607-629, February.
    • Handle: RePEc:bla:jorssa:v:183:y:2020:i:2:p:607-629
    DOI: 10.1111/rssa.12522
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    References listed on IDEAS

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    1. Andrea Gabrio & Alexina J. Mason & Gianluca Baio, 2017. "Erratum to: Handling Missing Data in Within-Trial Cost-Effectiveness Analysis: A Review with Future Recommendations," PharmacoEconomics - Open, Springer, vol. 1(2), pages 143-143, June.
    2. Antonio R. Linero & Michael J. Daniels, 2015. "A Flexible Bayesian Approach to Monotone Missing Data in Longitudinal Studies With Nonignorable Missingness With Application to an Acute Schizophrenia Clinical Trial," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 45-55, March.
    3. Michael J. Daniels & Joseph W. Hogan, 2000. "Reparameterizing the Pattern Mixture Model for Sensitivity Analyses Under Informative Dropout," Biometrics, The International Biometric Society, vol. 56(4), pages 1241-1248, December.
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    6. Chenguang Wang & Michael J. Daniels, 2011. "A Note on MAR, Identifying Restrictions, Model Comparison, and Sensitivity Analysis in Pattern Mixture Models with and without Covariates for Incomplete Data," Biometrics, The International Biometric Society, vol. 67(3), pages 810-818, September.
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    8. 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.
    9. Simon G. Thompson & Richard M. Nixon, 2005. "How Sensitive Are Cost-Effectiveness Analyses to Choice of Parametric Distributions?," Medical Decision Making, , vol. 25(4), pages 416-423, July.
    10. 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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    12. Manuel Gomes & Edmond S.-W. Ng & Richard Grieve & Richard Nixon & James Carpenter & Simon G. Thompson, 2012. "Developing Appropriate Methods for Cost-Effectiveness Analysis of Cluster Randomized Trials," Medical Decision Making, , vol. 32(2), pages 350-361, March.
    13. Nicola J. Cooper & Alex J. Sutton & Miranda Mugford & Keith R. Abrams, 2003. "Use of Bayesian Markov Chain Monte Carlo Methods to Model Cost-of-Illness Data," Medical Decision Making, , vol. 23(1), pages 38-53, January.
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    17. J. T. Gaskins & M. J. Daniels & B. H. Marcus, 2016. "Bayesian Methods for Nonignorable Dropout in Joint Models in Smoking Cessation Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1454-1465, October.
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    1. Ângela Jornada Ben & Johanna M. Dongen & Mohamed El Alili & Martijn W. Heymans & Jos W. R. Twisk & Janet L. MacNeil-Vroomen & Maartje Wit & Susan E. M. Dijk & Teddy Oosterhuis & Judith E. Bosmans, 2023. "The handling of missing data in trial-based economic evaluations: should data be multiply imputed prior to longitudinal linear mixed-model analyses?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(6), pages 951-965, August.
    2. Mohamed El Alili & Johanna M. van Dongen & Jonas L. Esser & Martijn W. Heymans & Maurits W. van Tulder & Judith E. Bosmans, 2022. "A scoping review of statistical methods for trial‐based economic evaluations: The current state of play," Health Economics, John Wiley & Sons, Ltd., vol. 31(12), pages 2680-2699, December.
    3. Andrea Gabrio & Catrin Plumpton & Sube Banerjee & Baptiste Leurent, 2022. "Linear mixed models to handle missing at random data in trial‐based economic evaluations," Health Economics, John Wiley & Sons, Ltd., vol. 31(6), pages 1276-1287, June.
    4. Alexina J. Mason & Manuel Gomes & James Carpenter & Richard Grieve, 2021. "Flexible Bayesian longitudinal models for cost‐effectiveness analyses with informative missing data," Health Economics, John Wiley & Sons, Ltd., vol. 30(12), pages 3138-3158, December.

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