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Methods For Covariate Adjustment In Cost‐Effectiveness Analysis That Use Cluster Randomised Trials

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  • Manuel Gomes
  • Richard Grieve
  • Richard Nixon
  • Edmond S.‐W. Ng
  • James Carpenter
  • Simon G. Thompson

Abstract

Statistical methods have been developed for cost‐effectiveness analyses of cluster randomised trials (CRTs) where baseline covariates are balanced. However, CRTs may show systematic differences in individual and cluster‐level covariates between the treatment groups. This paper presents three methods to adjust for imbalances in observed covariates: seemingly unrelated regression with a robust standard error, a ‘two‐stage’ bootstrap approach combined with seemingly unrelated regression and multilevel models. We consider the methods in a cost‐effectiveness analysis of a CRT with covariate imbalance, unequal cluster sizes and a prognostic relationship that varied by treatment group. The cost‐effectiveness results differed according to the approach for covariate adjustment. A simulation study then assessed the relative performance of methods for addressing systematic imbalance in baseline covariates. The simulations extended the case study and considered scenarios with different levels of confounding, cluster size variation and few clusters. Performance was reported as bias, root mean squared error and CI coverage of the incremental net benefit. Even with low levels of confounding, unadjusted methods were biased, but all adjusted methods were unbiased. Multilevel models performed well across all settings, and unlike the other methods, reported CI coverage close to nominal levels even with few clusters of unequal sizes. Copyright © 2012 John Wiley & Sons, Ltd.

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  • Manuel Gomes & Richard Grieve & Richard Nixon & Edmond S.‐W. Ng & James Carpenter & Simon G. Thompson, 2012. "Methods For Covariate Adjustment In Cost‐Effectiveness Analysis That Use Cluster Randomised Trials," Health Economics, John Wiley & Sons, Ltd., vol. 21(9), pages 1101-1118, September.
    • Handle: RePEc:wly:hlthec:v:21:y:2012:i:9:p:1101-1118
    DOI: 10.1002/hec.2812
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    2. Adrian Gheorghe & Tracy Roberts & Karla Hemming & Melanie Calvert, 2015. "Evaluating the Generalisability of Trial Results: Introducing a Centre- and Trial-Level Generalisability Index," PharmacoEconomics, Springer, vol. 33(11), pages 1195-1214, November.
    3. 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).
    4. Manuel Gomes & Karla Díaz-Ordaz & Richard Grieve & Michael G. Kenward, 2013. "Multiple Imputation Methods for Handling Missing Data in Cost-effectiveness Analyses That Use Data from Hierarchical Studies," Medical Decision Making, , vol. 33(8), pages 1051-1063, November.
    5. Theodoros Mantopoulos & Paul M. Mitchell & Nicky J. Welton & Richard McManus & Lazaros Andronis, 2016. "Choice of statistical model for cost-effectiveness analysis and covariate adjustment: empirical application of prominent models and assessment of their results," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(8), pages 927-938, November.
    6. Helen A. Dakin & José Leal & Andrew Briggs & Philip Clarke & Rury R. Holman & Alastair Gray, 2020. "Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data," Medical Decision Making, , vol. 40(4), pages 460-473, May.
    7. Peter Makai & Willemijn Looman & Eddy Adang & René Melis & Elly Stolk & Isabelle Fabbricotti, 2015. "Cost-effectiveness of integrated care in frail elderly using the ICECAP-O and EQ-5D: does choice of instrument matter?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(4), pages 437-450, May.

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