Multivariate Clustered Data Analysis in Developmental Toxicity Studies

In this paper we review statistical methods for analyzing developmental toxicity data. Such data raise a number of challenges. Models that try to accommodate the complex data generating mechanism of a developmental toxicity study, should take into account the litter effect and the number of viable fetuses, malformation indicators, weight and clustering, as a function of exposure. Further, the size of the litter may be related to outcomes among live fetuses. Scientific interest may be in inference about the dose effect, on implications of model misspecification, on assessment of model fit, and on the calculation of derived quantities such as safe limits, etc. We describe the relative merits of conditional, marginal and random-effects models for multivariate clustered binary data and present joint models for both continuous and discrete data.