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Exact Analysis of Dose Response for Multiple Correlated Binary Outcomes

Authors

  • Karen E. Han,

    Corresponding author
    1. Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, U.S.A.
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  • Paul J. Catalano,

    1. Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, U.S.A.
    2. Department of Biostatistical Science, Dana Farber Cancer Institute, Boston, Massachusetts 02115, U.S.A.
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  • Pralay Senchaudhuri,

    1. Cytel Software Corporation, Cambridge, Massachusetts 02139, U.S.A.
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  • Cyrus Mehta

    1. Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, U.S.A.
    2. Cytel Software Corporation, Cambridge, Massachusetts 02139, U.S.A.
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* email:khan@jimmy.harvard.edu

Abstract

Summary.  The neurotoxicity of a substance is often tested using animal bioassays. In the functional observational battery, animals are exposed to a test agent and multiple outcomes are recorded to assess toxicity, using approximately 40 animals measured on up to 30 different items. This design gives rise to a challenging statistical problem: a large number of outcomes for a small sample of subjects. We propose an exact test for multiple binary outcomes, under the assumption that the correlation among these items is equal. This test is based upon an exponential model described by Molenberghs and Ryan (1999, Environmetrics10, 279–300) and extends the methods developed by Corcoran et al. (2001, Biometrics57, 941–948) who developed an exact test for exchangeably correlated binary data for groups (clusters) of correlated observations. We present a method that computes an exact p-value testing for a joint dose–response relationship. An estimate of the parameter for dose response is also determined along with its 95% confidence bound. The method is illustrated using data from a neurotoxicity bioassay for the chemical perchlorethylene.

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