scholarly journals A computationally tractable multivariate random effects model for clustered binary data

Biometrika ◽  
2006 ◽  
Vol 93 (3) ◽  
pp. 587-599 ◽  
Author(s):  
Brent A. Coull ◽  
E. Andres Houseman ◽  
Rebecca A. Betensky
Keyword(s):  
Random Effects ◽  
Binary Data ◽  
Random Effects Model ◽  
Clustered Binary Data

scholarly journals Modeling overdispersed longitudinal binary data using a combined beta and normal random-effects model

2012 ◽  
Vol 70 (1) ◽  
Author(s):  
Wondwosen Kassahun ◽  
Thomas Neyens ◽  
Geert Molenberghs ◽  
Christel Faes ◽  
Geert Verbeke
Keyword(s):  
Random Effects ◽  
Binary Data ◽  
Random Effects Model ◽  
Longitudinal Binary Data

A Random Effects Model for Binary Data

Biometrics ◽  
1990 ◽  
Vol 46 (2) ◽  
pp. 317 ◽  
Author(s):  
Mark R. Conaway
Keyword(s):  
Random Effects ◽  
Binary Data ◽  
Random Effects Model

scholarly journals Modeling clustered binary data with excess zero clusters

2016 ◽  
Vol 27 (9) ◽  
pp. 2641-2656
Author(s):  
John Kwagyan ◽  
Victor Apprey
Keyword(s):  
Random Effects ◽  
Binary Data ◽  
Latent Class ◽  
Marginal Likelihood ◽  
Gaussian Quadrature ◽  
Gaussian Model ◽  
Likelihood Estimation ◽  
Clustered Binary Data ◽  
Random Effects Models ◽  
Cancer Data

We establish a zero-inflated (random-effects) logistic-Gaussian model for clustered binary data in which members of clusters in one latent class have a zero response with probability one, and members of clusters in a second latent class yield correlated outcomes. Response probabilities in terms of random-effects models are formulated, and maximum marginal likelihood estimation procedures based on Gaussian quadrature are developed. Application to esophageal cancer data in Chinese families is presented.

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