STATISTICAL METHODS FOR COST-EFFECTIVENESS ANALYSES THAT USE OBSERVATIONAL DATA: A CRITICAL APPRAISAL TOOL AND REVIEW OF CURRENT PRACTICE

Authors

  • Noémi Kreif,

    1. Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK
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  • Richard Grieve,

    Corresponding author
    1. Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK
    • Correspondence to: Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London WC1H 9SH, UK. E-mail: Richard.Grieve@lshtm.ac.uk

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  • M. Zia Sadique

    1. Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK
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SUMMARY

Many cost-effectiveness analyses (CEAs) use data from observational studies. Statistical methods can only address selection bias if they make plausible assumptions. No quality assessment tool is available for appraising CEAs that use observational studies. We developed a new checklist to assess statistical methods for addressing selection bias in CEAs that use observational data.

The checklist criteria were informed by a conceptual review and applied in a systematic review of economic evaluations. Criteria included whether the study assessed the ‘no unobserved confounding’ assumption, overlap of baseline covariates between the treatment groups and the specification of the regression models. The checklist also considered structural uncertainty from the choice of statistical approach.

We found 81 studies that met the inclusion criteria: studies tended to use regression (51%), matching on individual covariates (25%) or matching on the propensity score (22%). Most studies (77%) did not assess the ‘no observed confounding’ assumption, and few studies (16%) fully considered structural uncertainty from the choice of statistical approach.

We conclude that published CEAs do not assess the main assumptions behind statistical methods for addressing selection bias. This checklist can raise awareness about the assumptions behind statistical methods for addressing selection bias and can complement existing method guidelines for CEAs. Copyright © 2012 John Wiley & Sons, Ltd.

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