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Flexible Quantitative Risk Assessment for Developmental Toxicity Based on Fractional Polynomial Predictors
Author(s) -
Geys Helena,
Molenberghs Geert,
Declerck Lieven,
Ryan Louise
Publication year - 2000
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/1521-4036(200007)42:3<279::aid-bimj279>3.0.co;2-f
Subject(s) - developmental toxicity , multivariate statistics , inference , statistics , risk assessment , multivariate analysis , mathematics , computer science , econometrics , artificial intelligence , biology , pregnancy , genetics , gestation , computer security
Risk assessment for developmental toxicity studies in rodents is faced with the fairly involved data structure of clustered multivariate binary outcomes. While likelihood methods for this setting do not abound, we show that a conditional model, combined with pseudo‐likelihood inference and fractional polynomial predictor functions, as proposed by Royston and Altman (1994), are a promising way forward. The methods are illustrated using teratology data collected under the National Toxicology Program.