Crossed Random Effect Models for Multiple Outcomes in a Study of Teratogenesis

• Published in: Journal of the American Statistical Association Vol. 96; no. 456; pp. 1194 - 1204
• Main Authors: Coull, Brent A, Hobert, James P, Ryan, Louise M, Holmes, Lewis B
• Format: Journal Article
• Language: English
• Published: Alexandria, VA Taylor & Francis 01.12.2001; American Statistical Association
• Subjects: Algorithms; Anticonvulsants; Applications; Applications and Case Studies; Exact sciences and technology; Foetus; Generalized linear mixed model; Health outcomes; Human development; Infants; Lasso; Linear inference, regression; Linear models; Logistic regression; Markov chain Monte Carlo; Markov processes; Mathematics; Medical sciences; Modeling; Monte Carlo EM algorithm; Monte Carlo Newton-Raphson; Monte Carlo simulation; Multilevel models; Numerical analysis; Numerical analysis. Scientific computation; Numerical methods in probability and statistics; Parametric models; Probability and statistics; Probability theory and stochastic processes; Sciences and techniques of general use; Standard error; Statistical methods; Statistical models; Statistics; Teratogenesis; Teratology; Monte Carlo method; Generalized linear model; Statistical analysis; Diagnostic program; In utero; Lognormal distribution; Newton Raphson method; Markov chain; Teratogenesis; Logistic regression; Fetal development; MCMC algorithm; Regression model; Mixed model; Random effect; Numerical simulation; MCEM algoritm

Human teratogens often manifest themselves through a broad spectrum of adverse effects. Although often not serious when considered individually, such outcomes taken together may represent a syndrome that can lead to serious developmental problems. Accordingly, studies that investigate the effect of...