Flexible quantitative risk assessment for developmental toxicity based on fractional polynomial predictors

Abstract

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.