Marginal Modelling of Categorical Data from Crossover Experiments

Cecile C. Balagtas, Mark P. Becker and Joseph B. Lang

Journal of the Royal Statistical Society Series C, 1995, vol. 44, issue 1, 63-77

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.

Date: 1995
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.2307/2986195

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:44:y:1995:i:1:p:63-77

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().