IDEAS home Printed from
   My bibliography  Save this article

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


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


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.

Suggested Citation

  • 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

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Richard Grieve & Richard Nixon & Simon G. Thompson & John Cairns, 2007. "Multilevel models for estimating incremental net benefits in multinational studies," Health Economics, John Wiley & Sons, Ltd., vol. 16(8), pages 815-826, August.
    2. James R. Carpenter & Harvey Goldstein & Jon Rasbash, 2003. "A novel bootstrap procedure for assessing the relationship between class size and achievement," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(4), pages 431-443, October.
    3. Juxin Liu & Paul Gustafson, 2008. "On Average Predictive Comparisons and Interactions," International Statistical Review, International Statistical Institute, vol. 76(3), pages 419-432, December.
    4. Claxton, Karl, 1999. "The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies," Journal of Health Economics, Elsevier, vol. 18(3), pages 341-364, June.
    5. Jeffrey S. Hoch & Andrew H. Briggs & Andrew R. Willan, 2002. "Something old, something new, something borrowed, something blue: a framework for the marriage of health econometrics and cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 11(5), pages 415-430, July.
    6. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    7. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    8. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    9. Thompson, Simon G. & Nixon, Richard M. & Grieve, Richard, 2006. "Addressing the issues that arise in analysing multicentre cost data, with application to a multinational study," Journal of Health Economics, Elsevier, vol. 25(6), pages 1015-1028, November.
    10. Anirban Basu & James J. Heckman & Salvador Navarro-Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self-selection: an application to treatments of breast cancer patients," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1133-1157.
    11. Kosuke Imai & Gary King & Elizabeth A. Stuart, 2008. "Misunderstandings between experimentalists and observationalists about causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 481-502, April.
    12. Carpenter, James R. & Goldstein, Harvey & Kenward, Michael G., 2011. "REALCOM-IMPUTE Software for Multilevel Multiple Imputation with Mixed Response Types," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i05).
    13. Richard M. Nixon & Simon G. Thompson, 2005. "Methods for incorporating covariate adjustment, subgroup analysis and between‐centre differences into cost‐effectiveness evaluations," Health Economics, John Wiley & Sons, Ltd., vol. 14(12), pages 1217-1229, December.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. 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.
    2. 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.
    3. 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.

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:hlthec:v:21:y:2012:i:9:p:1101-1118. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley Content Delivery). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.