TY - JOUR

T1 - Simultaneous comparisons of treatments at multiple time points: combined marginal models versus joint modeling

AU - Pallmann, Philip

AU - Pretorius, Mias

AU - Ritz, Christian

N1 - 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/

PY - 2017/12/1

Y1 - 2017/12/1

N2 - 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.

AB - 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.

KW - Longitudinal data

KW - repeated measurements

KW - generalized least squares

KW - linear mixed-effects model

KW - AICc

U2 - 10.1177/0962280215603743

DO - 10.1177/0962280215603743

M3 - Journal article

VL - 26

SP - 2633

EP - 2648

JO - Statistical Methods in Medical Research

JF - Statistical Methods in Medical Research

SN - 0962-2802

IS - 6

ER -