Journal article
Marginal Modelling of Categorical Data from Crossover Experiments
Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol.44(1), pp.63-77
1995
DOI: 10.2307/2986195
Abstract
SUMMARY
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.
Details
- Title: Subtitle
- Marginal Modelling of Categorical Data from Crossover Experiments
- Creators
- Cecile C Balagtas - Warner‐Lambert Company Ann Arbor USAMark P Becker - University of MichiganJoseph B Lang - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol.44(1), pp.63-77
- DOI
- 10.2307/2986195
- ISSN
- 0035-9254
- eISSN
- 1467-9876
- Number of pages
- 15
- Language
- English
- Date published
- 1995
- Academic Unit
- Statistics and Actuarial Science; Biostatistics
- Record Identifier
- 9984257602902771
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