title: Marginal modeling of binary cross-over data.
abstract: A model specified in terms of linear models for marginal logits and linear models for log-odds ratios is proposed for the analysis of two-period binary cross-over experiments. Hypothesis testing and parameter estimation are facilitated by standard likelihood methodology. Two examples are used to illustrate how the model can be used to analyze two-period binary cross-over experiments. Results from a simulation study demonstrate that this approach to the analysis of binary cross-over data compares favorably with standard procedures, such as the Mainland-Gart test for a treatment difference, Prescott's test for a treatment difference, and the Hills-Armitage test for treatment-by-period interaction.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor 48109-2029.
authors.forename: M P
authors.initials: MP
authors.lastname: Becker
authors.forename: C C
authors.initials: CC
authors.lastname: Balagtas
mesh_terms: D001699:Biometry / Q000379:methods; D002561:Cerebrovascular Disorders / Q000188:drug therapy; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D015233:Models, Statistical; D016017:Odds Ratio; D012984:Software