Marginal modelling of multivariate categorical data.
This paper describes likelihood methods of analysis for multivariate categorical data. The joint distribution is specified in terms of marginal mean functions, and pairwise and higher order association measures. For the association, the emphasis is on global odds ratios. The method allows flexible formulation of a broad class of designs, such as repeated measurements, longitudinal studies, interrater agreement and cross-over trials. The proposed model can be used for parameter estimation and hypothesis testing. Simple fitting algorithms are proposed. The method is illustrated using a data example.
Molenberghs
G
G
Biostatistics, Limburgs Universitair Centrum, B3590 Diepenbeek, Belgium. geert.molenberghs@luc.ac.be
Lesaffre
E
E
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D002648:Child; D018592:Cross-Over Studies; D003627:Data Interpretation, Statistical; D005127:Eye Color; D005260:Female; D006200:Hair Color; D006261:Headache / Q000209:etiology; D017325:Hepatitis B Vaccines / Q000008:administration & dosage / Q000009:adverse effects; D006801:Humans; D007252:Influenza Vaccines / Q000008:administration & dosage / Q000009:adverse effects; D016013:Likelihood Functions; D008137:Longitudinal Studies; D008297:Male; D008954:Models, Biological; D015999:Multivariate Analysis; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D012120:Respiration Disorders / Q000209:etiology
D017325:Hepatitis B Vaccines; D007252:Influenza Vaccines