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A Random Effects Model for Binary Data from Crossover Clinical Trials

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  • Farkad Ezzet
  • John Whitehead

Abstract

The use of a random effects model for binary data in the interpretation of crossover studies is described. The model incorporates normally distributed subject effects, common to all responses from the same subject, into the linear part of the logistic regression model. The case of two treatments and two periods is considered, although extensions of the methodology to more general cases are possible. The paper describes how the model can be fitted and how the results can be interpreted. It is shown how data from subjects who miss the second period of treatment can be included in the analysis. Implications of the model on sample size calculations are studied, and a table to aid such calculations is provided. The methodology is illustrated with data from a recent pharmarceutical study of inhalation devices.

Suggested Citation

  • Farkad Ezzet & John Whitehead, 1992. "A Random Effects Model for Binary Data from Crossover Clinical Trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(1), pages 117-126, March.
    • Handle: RePEc:bla:jorssc:v:41:y:1992:i:1:p:117-126
    DOI: 10.2307/2347622
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    Cited by:

    1. Lui, Kung-Jong & Cumberland, William G. & Chang, Kuang-Chao, 2014. "Notes on testing equality in binary data under a three period crossover design," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 89-98.
    2. Mingan Yang, 2018. "Assessment of Noninferiority (and Equivalence) for Simple Crossover Trials Using Bayesian Approach," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(3), pages 506-519, December.
    3. Lui, Kung-Jong & Chang, Kuang-Chao, 2012. "Estimation of the proportion ratio under a simple crossover trial," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 522-530.

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