Likelihood Models for Clustered Binary and Continuous Out comes: Application to Developmental Toxicology

DOI10.1111/j.0006-341X.1999.00760.xzbMath1059.62695WikidataQ52136461 ScholiaQ52136461MaRDI QIDQ4666642

Paul J. Catalano, Meredith M. Regan

Publication date: 13 April 2005

Published in: Biometrics (Search for Journal in Brave)

zbMATH Keywords

probit modelexchangeabilitymaximum likelihoodrisk assessmentdose-response modeling

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Estimation in survival analysis and censored data (62N02)

Related Items (17)

Regression models for analyzing clustered binary and continuous outcomes under an assumption of exchangeability ⋮ Selection of Latent Variables for Multiple Mixed-outcome Models ⋮ Risk assessment for toxicity experiments with discrete and continuous outcomes: a Bayesian nonparametric approach ⋮ Multivariate generalized linear mixed models with random intercepts to analyze cardiovascular risk markers in type-1 diabetic patients ⋮ A Bayesian analysis of clustered discrete and continuous outcomes ⋮ Gaussian copula mixed models for clustered mixed outcomes, with application in developmental toxicology ⋮ Quantitative Risk Assessment for Multivariate Continuous Outcomes with Application to Neurotoxicology: The Bivariate Case ⋮ Association Models for Clustered Data with Binary and Continuous Responses ⋮ Construction of random vectors of heterogeneous component variables under specified correlation structures ⋮ Bayesian estimation of random effects models for multivariate responses of mixed data ⋮ Test of Marginal Compatibility and Smoothing Methods for Exchangeable Binary Data with Unequal Cluster Sizes ⋮ Joint modelling of mixed outcome types using latent variables ⋮ Estimation of a Two-Equation Panel Model with Mixed Continuous and Ordered Categorical Outcomes and Missing Data ⋮ A Bayesian Approach for Joint Modeling of Cluster Size and Subunit-Specific Outcomes ⋮ A hierarchical modeling approach for risk assessment in developmental toxicity studies ⋮ A semivarying joint model for longitudinal binary and continuous outcomes ⋮ Lymphangiogenesis and carcinoma in the uterine cervix: joint and hierarchical models for random cluster sizes and continuous outcomes

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• Response models for mixed binary and quantitative variables
• Tree-Structured Logistic Model for Over-Dispersed Binomial Data with Application to Modeling Developmental Effects
• Regression Models for a Bivariate Discrete and Continuous Outcome with Clustering

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