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Marginal Modelling of Categorical Data from Crossover Experiments

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  • Cecile C. Balagtas
  • Mark P. Becker
  • Joseph B. Lang

Abstract

Marginal models provide a useful framework for the analysis of crossover experiments when the response variable is categorical. In this paper we use the three‐treatment, three‐period crossover experiment with a binary outcome variable to demonstrate how marginal models can be used to perform a likelihood‐based analysis of multiple‐period crossover experiments. Other designs are discussed in less detail. Maximum likelihood estimation is performed using a constraint equation specification of the marginal model. Data from a crossover trial comparing treatments for primary dysmenorrhoea are used to demonstrate the utility of marginal models in analysing crossover data.

Suggested Citation

  • Cecile C. Balagtas & Mark P. Becker & Joseph B. Lang, 1995. "Marginal Modelling of Categorical Data from Crossover Experiments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 44(1), pages 63-77, March.
    • Handle: RePEc:bla:jorssc:v:44:y:1995:i:1:p:63-77
    DOI: 10.2307/2986195
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    Cited by:

    1. Tamás Rudas & Wicher P. Bergsma, 2004. "On applications of marginal models for categorical data," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 15-37.
    2. Joseph B. Lang & Scott R. Eliason, 1997. "Application of Association-Marginal Models to the Study of Social Mobility," Sociological Methods & Research, , vol. 26(2), pages 183-212, November.

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