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    Rights statement: The final, definitive version of this article has been published in the Journal, Statistical Methods in Medical Research, 26 (6), 2017, © SAGE Publications Ltd, 2017 by SAGE Publications Ltd at the Statistical Methods in Medical Research page: http://journals.sagepub.com/home/smm on SAGE Journals Online: http://journals.sagepub.com/

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Simultaneous comparisons of treatments at multiple time points: combined marginal models versus joint modeling

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<mark>Journal publication date</mark>1/12/2017
<mark>Journal</mark>Statistical Methods in Medical Research
Issue number6
Volume26
Number of pages16
Pages (from-to)2633-2648
Publication StatusPublished
Early online date18/09/15
<mark>Original language</mark>English

Abstract

We discuss several aspects of multiple inference in longitudinal settings, focusing on many-to-one and all-pairwise comparisons of (a) treatment groups simultaneously at several points in time, or (b) time points simultaneously for several treatments. We assume a continuous endpoint that is measured repeatedly over time and contrast two basic modeling strategies: fitting a joint model across all occasions (with random effects and/or some residual covariance structure to account for heteroscedasticity and serial dependence), and a novel approach combining a set of simple marginal, i.e. occasion-specific models. Upon parameter and covariance estimation with either modeling approach, we employ a variant of multiple contrast tests that acknowledges correlation between time points and test statistics. This method provides simultaneous confidence intervals and adjusted p-values for elementary hypotheses as well as a global test decision. We compare via simulation the powers of multiple contrast tests based on a joint model and multiple marginal models, respectively, and quantify the benefit of incorporating longitudinal correlation, i.e. the advantage over Bonferroni. Practical application is illustrated with data from a clinical trial on bradykinin receptor antagonism.

Bibliographic note

The final, definitive version of this article has been published in the Journal, Statistical Methods in Medical Research, 26 (6), 2017, © SAGE Publications Ltd, 2017 by SAGE Publications Ltd at the Statistical Methods in Medical Research page: http://journals.sagepub.com/home/smm on SAGE Journals Online: http://journals.sagepub.com/