ABSTRACT
BACKGROUND: In developmental and reproductive toxicity studies, analysis of litter-based binary endpoints (e.g., incidence of malformed fetuses) is complex in that littermates often are not entirely independent of one another. It is well established that the litter, not the individual fetus, is the proper independent experimental unit in statistical analysis. Accordingly, analysis is often based on the proportion affected per litter and the litter proportions are analyzed as continuous data. Because these proportional data generally do not meet assumptions of symmetry or normality, data are typically analyzed by nonparametric methods, arcsine square root transformation, or logit transformation. METHODS: We conducted power calculations to compare different approaches (nonparametric, arcsine square root-transformed, logit-transformed, untransformed) for analyzing litter-based proportional data. A reproductive toxicity study with a control and one treated group provided data for two endpoints: prenatal loss, and fertility by in utero insemination (IUI). Type 1 error and power were estimated by 10,000 simulations based on two-sample one-tailed t tests with varying numbers of litters per group. To further compare the different approaches, we conducted additional analyses with shifted mean proportions to produce illustrative scenarios. RESULTS: Analyses based on logit-transformed proportions had greater power than those based on untransformed or arcsine square root-transformed proportions, or nonparametric procedures. CONCLUSION: The logit transformation is preferred to the other approaches considered when making inferences concerning litter-based proportional endpoints, particularly with skewed distributions. The improved performance of the logit transformation becomes increasingly pronounced as the response proportions are increasingly close to the boundaries of the parameter space.
Subject(s)
Reproduction , Research Design , Female , Humans , Incidence , PregnancyABSTRACT
In rodent bioassays where chemicals are administered in the drinking water, water consumption data for individual animals are needed to estimate chemical exposures accurately. If multiple animals share a common water source, as occurs in some studies, only the total amount of drinking water consumed by all animals utilizing the common source is directly measurable, and water consumption rates for individual animals are not available. In the Four Lab Study of the US Environmental Protection Agency, which included a multigenerational rodent bioassay, a complex mixture of drinking water disinfection by-products was delivered to multiple Sprague-Dawley rats from a common drinking water container. To estimate disinfection by-product mixture exposure for each animal, authors developed four log-linear regression models to allocate water consumption among rats sharing a common water container. The four models represented three animal lifestages: Gestation, Lactation, and Postweaning, with separate Postweaning models for male and female. Authors used data from six Sprague-Dawley rat bioassays to develop these models from available individual cage data for the Postweaning models, and available individual animal data for the Gestation and Lactation models. The r(2) values for the model fits were good, ranging from 0.67 to 0.92. The Gestation and Lactation models were generally quite accurate in predicting average daily water consumption whereas the Postweaning models were less robust. These models can be generalized for use in other reproductive and developmental bioassays where common water sources are used and data on the explanatory variables are available.
