Order-Restricted Inference for Multivariate Binary Data With Application to Toxicology

Abstract

In many applications, researchers collect multivariate binary response data under two or more naturally ordered experimental conditions. In such situations, one is often interested in using all binary outcomes simultaneously to detect an ordering among the experimental conditions. To make such comparisons, we develop a general methodology for testing for the multivariate stochastic order between K ≥ 2 multivariate binary distributions. Our proposed test uses order-restricted estimators, which, according to our simulation study, are more efficient than the unrestricted estimators in terms of their mean squared error. We compared the power of the proposed test with that of several alternative tests, including procedures that combine individual univariate tests for order, such as union-intersection tests and a Bonferroni-based test. We also compared the proposed test with an unrestricted Hotelling T 2 -type test. Our simulations suggest that the proposed method competes well with these alternatives. The gain in power is often substantial. The proposed methodology is illustrated by applying it to a two-year rodent cancer bioassay data obtained from the U.S. National Toxicology Program. Supplemental materials are available online.