A hierarchical modeling approach for risk assessment in developmental toxicity studies

Within the past decade, there has been an increasing interest in the problem of joint analysis of clustered multiple outcome data, motivated by developmental toxicity applications. Typically, a toxic insult early in gestation may result in a resorbed fetus or a fetal death. If however the fetus survives the entire gestation period, growth reduction such as low birth weight may occur, or can exhibit a malformation. Ideally, a model should take the complete hierarchical structure of the data into account. So far, however, one has tackled the challenges in this setting only partly each time making different restricting assumptions (e.g., restriction to viable fetuses only). In addition, because of genetic similarity and the same treatment conditions, offsprings of the same mother behave more alike than those of another mother. Thus, responses on different fetuses within a cluster are likely to be correlated. The ultimate scientific question requires assessing the full effect of dose administration, not only in the malformation and weight outcome, but also in the death outcome. A hierarchical Bayesian method is proposed to this effect. Such a model can serve as a basis for quantitative risk assessment.