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Methods for estimating complier average causal effects for cost‐effectiveness analysis

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  • K. DiazOrdaz
  • A. J. Franchini
  • R. Grieve

Abstract

In randomized controlled trials with treatment non‐compliance, instrumental variable approaches are used to estimate complier average causal effects. We extend these approaches to cost‐effectiveness analyses, where methods need to recognize the correlation between cost and health outcomes. We propose a Bayesian full likelihood approach, which jointly models the effects of random assignment on treatment received and the outcomes, and a three‐stage least squares method, which acknowledges the correlation between the end points and the endogeneity of the treatment received. This investigation is motivated by the REFLUX study, which exemplifies the setting where compliance differs between the randomized controlled trial and routine practice. A simulation is used to compare the methods’ performance. We find that failure to model the correlation between the outcomes and treatment received correctly can result in poor confidence interval coverage and biased estimates. By contrast, Bayesian full likelihood and three‐stage least squares methods provide unbiased estimates with good coverage.

Suggested Citation

  • K. DiazOrdaz & A. J. Franchini & R. Grieve, 2018. "Methods for estimating complier average causal effects for cost‐effectiveness analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(1), pages 277-297, January.
    • Handle: RePEc:bla:jorssa:v:181:y:2018:i:1:p:277-297
    DOI: 10.1111/rssa.12294
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    Cited by:

    1. Deidda, Manuela & Geue, Claudia & Kreif, Noemi & Dundas, Ruth & McIntosh, Emma, 2019. "A framework for conducting economic evaluations alongside natural experiments," Social Science & Medicine, Elsevier, vol. 220(C), pages 353-361.
    2. David M. Phillippo & Sofia Dias & A. E. Ades & Mark Belger & Alan Brnabic & Alexander Schacht & Daniel Saure & Zbigniew Kadziola & Nicky J. Welton, 2020. "Multilevel network meta‐regression for population‐adjusted treatment comparisons," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1189-1210, June.

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