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Multiple Contrast Tests for Multiple Endpoints in the Presence of Heteroscedasticity

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  • Hasler Mario

    (Christian-Albrechts-University of Kiel, Schleswig-Holstein D-24118, Germany)

Abstract

This article describes an extension of multiple contrast tests to the case of multiple, correlated endpoints. These endpoints are assumed to be normally distributed with different scales and variances. Unlike in previous articles, the covariance matrices are also assumed to be unequal for the treatment groups. Approximate multivariate t-distributions are used to obtain multiplicity-adjusted p-values and quantiles for test decisions or simultaneous confidence intervals. Simulation results show that this approach controls the family-wise error type I over both the comparisons and the endpoints in an admissible range. The approach will be applied to a semi-synthetic example data set of a randomized, placebo-controlled phase IIb dose-finding study of a novel anti-muscarinic agent for five continuous endpoints. A related R package is available.

Suggested Citation

  • Hasler Mario, 2014. "Multiple Contrast Tests for Multiple Endpoints in the Presence of Heteroscedasticity," The International Journal of Biostatistics, De Gruyter, vol. 10(1), pages 17-28, May.
    • Handle: RePEc:bpj:ijbist:v:10:y:2014:i:1:p:12:n:1
    DOI: 10.1515/ijb-2012-0015
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    References listed on IDEAS

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    1. Solomon Harrar & Arne Bathke, 2012. "Erratum to: A modified two-factor multivariate analysis of variance: asymptotics and small sample approximations," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(5), pages 1087-1087, October.
    2. Solomon Harrar & Arne Bathke, 2012. "A modified two-factor multivariate analysis of variance: asymptotics and small sample approximations," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(1), pages 135-165, February.
    3. Harrar, Solomon W. & Bathke, Arne C., 2008. "Nonparametric methods for unbalanced multivariate data and many factor levels," Journal of Multivariate Analysis, Elsevier, vol. 99(8), pages 1635-1664, September.
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