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Methods for Generating Longitudinally Correlated Binary Data
Author(s) -
Farrell Patrick J.,
RogersStewart Katrina
Publication year - 2008
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/j.1751-5823.2007.00017.x
Subject(s) - estimator , binary data , binary number , computer science , flexibility (engineering) , contrast (vision) , range (aeronautics) , sample (material) , longitudinal data , sample size determination , statistics , data mining , algorithm , mathematics , econometrics , artificial intelligence , arithmetic , materials science , chemistry , chromatography , composite material
Summary 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.