Some general methods for the analysis of categorical data in longitudinal studies.
This paper is concerned with the analysis of multivariate categorical data from epidemiologic and clinical studies with longitudinal designs. An expository discussion of pertinent hypotheses for such situations is provided within the context of two relevant data sets. Appropriate large-sample tests of these hypotheses are developed through the application of weighted least squares to generate Wald statistics. These procedures are illustrated with extensive analyses of one of these data sets. In some situations, the resulting cross-classification of the response variables leads to extremely sparse frequency data, especially when the number of subjects is not large. For such repeated measurement designs in which a single variable is measured repeatedly over time, this paper considers the use of a generalized Mantel-Haenszel strategy for tests of marginal homogeneity (symmetry). These randomization model methods are illustrated for data in which the repeated measurement variable is reported on an ordinal scale. This paper also focuses on the available computing software to implement these methods within the version 5 release of the SAS system. The randomization model approach can be implemented within the FREQ procedure and a broad range of models and hypotheses can be investigated within the CATMOD procedure.
Landis
J R
JR
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor 48109.
Miller
M E
ME
Davis
C S
CS
Koch
G G
GG
D000818:Animals; D002986:Clinical Trials as Topic; D004285:Dogs; D004556:Electric Injuries / Q000503:physiopathology; D006801:Humans; D008137:Longitudinal Studies; D008954:Models, Biological; D011897:Random Allocation; D012871:Skin Diseases / Q000188:drug therapy; D013223:Statistics as Topic; D013997:Time Factors