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Marginalizable Conditional Model for Clustered Ordinal Data by Zhang, Rui; Chan, Kwun Chuen Gary is a scholarly article available to read on EtoBox.

What is Marginalizable Conditional Model for Clustered Ordinal Data about?

We introduce a flexible parametric mixed effects model for correlated binary data, with parameters that can be directly interpreted as marginal odds ratios. This leads to a robust estimation equation with an optimal weighting matrix being the inverse of a genuine model-based covariance matrix. Flexible correlation structures can be imposed by correlated random effects, and correlation parameters can be estimated by solving a composite likelihood score function. Marginal parameters are consistently estimated even when the conditional parametric model is misspecified, and the robust estimation procedure has low estimation efficiency loss compared to the maximum likelihood estimation under a correct model specification. Simulations, analyses of the Madras longitudinal schizophrenia study and British social attributes panel survey were carried out to demonstrate our method.

Author

Zhang, Rui; Chan, Kwun Chuen Gary

Published

2014

Language

EN