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A generalization of the Grizzle model to the estimation of treatment effects in crossover trials with non-compliance

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  • Ali Reza Soltanian
  • Soghrat Faghihzadeh

Abstract

Compliance with one specified dosing strategy of assigned treatments is a common problem in randomized drug clinical trials. Recently, there has been much interest in methods used for analysing treatment effects in randomized clinical trials that are subject to non-compliance. In this paper, we estimate and compare treatment effects based on the Grizzle model (GM) (ignorable non-compliance) as the custom model and the generalized Grizzle model (GGM) (non-ignorable non-compliance) as the new model. A real data set based on the treatment of knee osteoarthritis is used to compare these models. The results based on the likelihood ratio statistics and simulation study show the advantage of the proposed model (GGM) over the custom model (GGM).

Suggested Citation

  • Ali Reza Soltanian & Soghrat Faghihzadeh, 2012. "A generalization of the Grizzle model to the estimation of treatment effects in crossover trials with non-compliance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(5), pages 1037-1048, October.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:1037-1048
    DOI: 10.1080/02664763.2011.634396
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    References listed on IDEAS

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    4. S. Vansteelandt & E. Goetghebeur, 2003. "Causal inference with generalized structural mean models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(4), pages 817-835, November.
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