Regression models for analyzing clustered binary and continuous outcomes under an assumption of exchangeability |
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Authors: | E Olusegun George Dale Armstrong Paul J Catalano Deo Kumar Srivastava |
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Institution: | 1. Department of Mathematical Sciences, The University of Memphis, Memphis, TN 38152, USA;2. Department of Mathematics, The University of Mississippi, Oxford, MS 38677, USA;3. Department of Biostatistics, Harvard School of Public Health and Dana–Farber Cancer Institute, Boston, MA 02115, USA;4. Department of Biostatistics, St. Jude Children''s Research Hospital, Memphis, TN 38105, USA |
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Abstract: | Scientific experiments commonly result in clustered discrete and continuous data. Existing methods for analyzing such data include the use of quasi-likelihood procedures and generalized estimating equations to estimate marginal mean response parameters. In applications to areas such as developmental toxicity studies, where discrete and continuous measurements are recorded on each fetus, or clinical ophthalmologic trials, where different types of observations are made on each eye, the assumption that data within cluster are exchangeable is often very reasonable. We use this assumption to formulate fully parametric regression models for clusters of bivariate data with binary and continuous components. The regression models proposed have marginal interpretations and reproducible model structures. Tractable expressions for likelihood equations are derived and iterative schemes are given for computing efficient estimates (MLEs) of the marginal mean, correlations, variances and higher moments. We demonstrate the use the ‘exchangeable’ procedure with an application to a developmental toxicity study involving fetal weight and malformation data. |
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Keywords: | 62J99 62P12 62H99 |
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