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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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Simultaneous comparisons of treatments at multiple time points: combined marginal models versus joint modeling. / Pallmann, Philip; Pretorius, Mias; Ritz, Christian.

In: Statistical Methods in Medical Research, Vol. 26, No. 6, 01.12.2017, p. 2633-2648.

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Pallmann, Philip ; Pretorius, Mias ; Ritz, Christian. / Simultaneous comparisons of treatments at multiple time points: combined marginal models versus joint modeling. In: Statistical Methods in Medical Research. 2017 ; Vol. 26, No. 6. pp. 2633-2648.

Bibtex

@article{42f89025fec94aa2824569d1a23fe53f,
title = "Simultaneous comparisons of treatments at multiple time points: combined marginal models versus joint modeling",
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. ",
keywords = "Longitudinal data, repeated measurements, generalized least squares, linear mixed-effects model, AICc",
author = "Philip Pallmann and Mias Pretorius and Christian Ritz",
note = "The final, definitive version of this article has been published in the Journal, Statistical Methods in Medical Research, 26 (6), 2017, {\textcopyright} 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/",
year = "2017",
month = dec,
day = "1",
doi = "10.1177/0962280215603743",
language = "English",
volume = "26",
pages = "2633--2648",
journal = "Statistical Methods in Medical Research",
issn = "0962-2802",
publisher = "SAGE Publications Ltd",
number = "6",

}

RIS

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 -