AN EVALUATION OF SOME METHODS FOR FITTING DOSE-RESPONSE MODELS TO QUANTAL-RESPONSE DEVELOPMENTAL TOXICOLOGY DATA

The analysis of quantal-response developmental toxicology data by dose-response modeling is discussed, with emphasis on methods that avoid exact distributional assumptions. These methods (quasi-likelihood, bootstrapping, and jackknifing) are contrasted with analyses based on the beta-binomial distribution. For the resampling procedures, dose-response models are fit under a binomial likelihood. A justification for this choice of estimator in resampling plans is given, based on an extension of the standard results for asymptotic normality and consistency of maximum likelihood estimators. This justification depends only on the true distribution of the data having the usual binomial expectation. A quasi-likelihood approach is also considered, in which simple assumptions about the intralitter correlation structure are made. Quasi-likelihood methods are in theory asymptotically robust to misspecification of the intralitter correlation structure. The practical implications of these asymptotic results are evaluated in a simulation study.