Estimating herd-specific force of infection by using random-effects models for clustered binary data and monotone fractional polynomials

2006 ◽  
Vol 55 (5) ◽  
pp. 595-613 ◽  
Author(s):  
Christel Faes ◽  
Niel Hens ◽  
Marc Aerts ◽  
Ziv Shkedy ◽  
Helena Geys ◽  
...  
Keyword(s):  
Random Effects ◽  
Binary Data ◽  
Specific Force ◽  
Fractional Polynomials ◽  
Clustered Binary Data ◽  
Random Effects Models ◽  
Force Of Infection

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.

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 Intra-class Correlation in Random-effects Models for Binary Data

2003 ◽  
Vol 3 (1) ◽  
pp. 32-46 ◽  
Author(s):  
Germán Rodríguez ◽  
Irma Elo
Keyword(s):  
Random Effects ◽  
Latent Variable ◽  
Binary Data ◽  
Binary Outcomes ◽  
Measures Of Association ◽  
Intra Class Correlation ◽  
Random Effects Models ◽  
Alternative Measures

We review the concept of intra-class correlation in random-effects models for binary outcomes as estimated by Stata's xtprobit, xtlogit, and xtclog. We consider the usual measures of correlation based on a latent variable formulation of these models and note corrections to the last two procedures. We also discuss alternative measures of association based on manifest variables or actual outcomes and introduce a new command xtrho for computing these measures for all three types of models.

Marginalized random effects models for multivariate longitudinal binary data

2009 ◽  
Vol 28 (8) ◽  
pp. 1284-1300 ◽  
Author(s):  
Keunbaik Lee ◽  
Yongsung Joo ◽  
Jae Keun Yoo ◽  
JungBok Lee
Keyword(s):  
Random Effects ◽  
Binary Data ◽  
Random Effects Models ◽  
Longitudinal Binary Data
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