The analysis of longitudinally correlated binary data has attracted considerable attention of late. Since the estimation of parameters in models for such data is based on asymptotic theory, it is necessary to investigate the small-sample properties of estimators by simulation. In this paper, we review the mechanisms that have been proposed for generating longitudinally correlated binary data. We compare and contrast these models with regard to various features, including computational efficiency, flexibility and the range restrictions that they impose on the longitudinal association parameters. Some extensions to the data generation mechanism originally suggested by Kanter (1975) are proposed.

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International Statistical Review
School of Mathematics and Statistics

Farrell, P, & Rogers-Stewart, K. (Katrina). (2008). Methods for generating longitudinally correlated binary data. International Statistical Review, 76(1), 28–38. doi:10.1111/j.1751-5823.2007.00017.x