### Abstract

Marginal models and conditional mixed-effects models are commonly used for clustered binary data. However, regression parameters and predictions in nonlinear mixed-effects models usually do not have a direct marginal interpretation, because the conditional functional form does not carry over to the margin. Because both marginal and conditional inferences are of interest, a unified approach is attractive. To this end, we investigate a parameterization of generalized linear mixed models with a structured random-intercept distribution that matches the conditional and marginal shapes. We model the marginal mean of response distribution and select the distribution of the random intercept to produce the match and also to model covariate-dependent random effects. We discuss the relation between this approach and some existing models and compare the approaches on two datasets.

Original language | English (US) |
---|---|

Pages (from-to) | 884-891 |

Number of pages | 8 |

Journal | Biometrics |

Volume | 60 |

Issue number | 4 |

DOIs | |

State | Published - Dec 2004 |

### Fingerprint

### Keywords

- Bridge distribution
- Clustered data
- Gaussian-Hermite quadrature
- Marginal model
- Random-effects model

### ASJC Scopus subject areas

- Statistics and Probability
- Medicine(all)
- Immunology and Microbiology(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Agricultural and Biological Sciences(all)
- Applied Mathematics

### Cite this

**Marginalized binary mixed-effects models with covariate-dependent random effects and likelihood inference.** / Wang, Zengri; Louis, Thomas.

Research output: Contribution to journal › Article

*Biometrics*, vol. 60, no. 4, pp. 884-891. https://doi.org/10.1111/j.0006-341X.2004.00243.x

}

TY - JOUR

T1 - Marginalized binary mixed-effects models with covariate-dependent random effects and likelihood inference

AU - Wang, Zengri

AU - Louis, Thomas

PY - 2004/12

Y1 - 2004/12

N2 - Marginal models and conditional mixed-effects models are commonly used for clustered binary data. However, regression parameters and predictions in nonlinear mixed-effects models usually do not have a direct marginal interpretation, because the conditional functional form does not carry over to the margin. Because both marginal and conditional inferences are of interest, a unified approach is attractive. To this end, we investigate a parameterization of generalized linear mixed models with a structured random-intercept distribution that matches the conditional and marginal shapes. We model the marginal mean of response distribution and select the distribution of the random intercept to produce the match and also to model covariate-dependent random effects. We discuss the relation between this approach and some existing models and compare the approaches on two datasets.

AB - Marginal models and conditional mixed-effects models are commonly used for clustered binary data. However, regression parameters and predictions in nonlinear mixed-effects models usually do not have a direct marginal interpretation, because the conditional functional form does not carry over to the margin. Because both marginal and conditional inferences are of interest, a unified approach is attractive. To this end, we investigate a parameterization of generalized linear mixed models with a structured random-intercept distribution that matches the conditional and marginal shapes. We model the marginal mean of response distribution and select the distribution of the random intercept to produce the match and also to model covariate-dependent random effects. We discuss the relation between this approach and some existing models and compare the approaches on two datasets.

KW - Bridge distribution

KW - Clustered data

KW - Gaussian-Hermite quadrature

KW - Marginal model

KW - Random-effects model

UR - http://www.scopus.com/inward/record.url?scp=10944236044&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=10944236044&partnerID=8YFLogxK

U2 - 10.1111/j.0006-341X.2004.00243.x

DO - 10.1111/j.0006-341X.2004.00243.x

M3 - Article

VL - 60

SP - 884

EP - 891

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 4

ER -