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Marginal Modelling of Categorical Data from Crossover Experiments
Author(s) -
Balagtas Cecile C.,
Becker Mark P.,
Lang Joseph B.
Publication year - 1995
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.2307/2986195
Subject(s) - crossover , categorical variable , marginal model , computer science , statistics , artificial intelligence , mathematics , regression analysis
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.