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

Pallmann, Philip ORCID:, Pretorius, Mias and Ritz, Christian 2017. Simultaneous comparisons of treatments at multiple time points: Combined marginal models versus joint modeling. Statistical Methods in Medical Research 26 (6) , pp. 2633-2648. 10.1177/0962280215603743

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
Publisher: SAGE
ISSN: 0962-2802
Last Modified: 03 Nov 2022 09:50

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