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Handling missing values in cost effectiveness analyses that use data from cluster randomized trials

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  • K. Díaz-Ordaz
  • Michael G. Kenward
  • Richard Grieve

Abstract

type="main" xml:id="rssa12016-abs-0001"> Public policy makers use cost effectiveness analyses (CEAs) to decide which health and social care interventions to provide. Missing data are common in CEAs, but most studies use complete-case analysis. Appropriate methods have not been developed for handling missing data in complex settings, exemplified by CEAs that use data from cluster randomized trials. We present a multilevel multiple-imputation approach that recognizes the hierarchical structure of the data and is compatible with the bivariate multilevel models that are used to report cost effectiveness. We contrast this approach with single-level multiple imputation and complete-case analysis, in a CEA alongside a cluster randomized trial. The paper highlights the importance of adopting a principled approach to handling missing values in settings with complex data structures.

Suggested Citation

  • K. Díaz-Ordaz & Michael G. Kenward & Richard Grieve, 2014. "Handling missing values in cost effectiveness analyses that use data from cluster randomized trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(2), pages 457-474, February.
    • Handle: RePEc:bla:jorssa:v:177:y:2014:i:2:p:457-474
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    File URL: http://hdl.handle.net/10.1111/rssa.2014.177.issue-2
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    Cited by:

    1. Manju, Md Abu & Candel, Math J.J.M. & van Breukelen, Gerard J.P., 2021. "Robustness of cost-effectiveness analyses of cluster randomized trials assuming bivariate normality against skewed cost data," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    2. Jackie, Yenerall & Wen, You & George, Davis & Paul, Estabrooks, 2015. "Examining Ways to Handle Non-Random Missingness in CEA through Econometric and Statistics Lenses," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205690, Agricultural and Applied Economics Association.
    3. 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.
    4. Rashid, S. & Mitra, R. & Steele, R.J., 2015. "Using mixtures of t densities to make inferences in the presence of missing data with a small number of multiply imputed data sets," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 84-96.

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