Simultaneous small-sample comparisons in longitudinal or multi-endpoint trials using multiple marginal models

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Standard

Simultaneous small-sample comparisons in longitudinal or multi-endpoint trials using multiple marginal models. / Pallmann, Philip; Ritz, Christian; Hothorn, Ludwig A.

I: Statistics in Medicine, Bind 37, Nr. 9, 2018, s. 1562-1576.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Pallmann, P, Ritz, C & Hothorn, LA 2018, 'Simultaneous small-sample comparisons in longitudinal or multi-endpoint trials using multiple marginal models', Statistics in Medicine, bind 37, nr. 9, s. 1562-1576. https://doi.org/10.1002/sim.7610

APA

Pallmann, P., Ritz, C., & Hothorn, L. A. (2018). Simultaneous small-sample comparisons in longitudinal or multi-endpoint trials using multiple marginal models. Statistics in Medicine, 37(9), 1562-1576. https://doi.org/10.1002/sim.7610

Vancouver

Pallmann P, Ritz C, Hothorn LA. Simultaneous small-sample comparisons in longitudinal or multi-endpoint trials using multiple marginal models. Statistics in Medicine. 2018;37(9):1562-1576. https://doi.org/10.1002/sim.7610

Author

Pallmann, Philip ; Ritz, Christian ; Hothorn, Ludwig A. / Simultaneous small-sample comparisons in longitudinal or multi-endpoint trials using multiple marginal models. I: Statistics in Medicine. 2018 ; Bind 37, Nr. 9. s. 1562-1576.

Bibtex

@article{9bd97838138642aba409a43de79fa7da,
title = "Simultaneous small-sample comparisons in longitudinal or multi-endpoint trials using multiple marginal models",
abstract = "Simultaneous inference in longitudinal, repeated-measures, and multi-endpoint designs can be onerous, especially when trying to find a reasonable joint model from which the interesting effects and covariances are estimated. A novel statistical approach known as multiple marginal models greatly simplifies the modelling process: the core idea is to {"}marginalise{"} the problem and fit multiple small models to different portions of the data, and then estimate the overall covariance matrix in a subsequent, separate step. Using these estimates guarantees strong control of the family-wise error rate, however only asymptotically. In this paper, we show how to make the approach also applicable to small-sample data problems. Specifically, we discuss the computation of adjusted P values and simultaneous confidence bounds for comparisons of randomised treatment groups as well as for levels of a nonrandomised factor such as multiple endpoints, repeated measures, or a series of points in time or space. We illustrate the practical use of the method with a data example.",
keywords = "Correlated data, Degrees of freedom, Linear mixed-effects model, Multiple contrast test",
author = "Philip Pallmann and Christian Ritz and Hothorn, {Ludwig A}",
note = "CURIS 2018 NEXS 070",
year = "2018",
doi = "10.1002/sim.7610",
language = "English",
volume = "37",
pages = "1562--1576",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "JohnWiley & Sons Ltd",
number = "9",

}

RIS

TY - JOUR

T1 - Simultaneous small-sample comparisons in longitudinal or multi-endpoint trials using multiple marginal models

AU - Pallmann, Philip

AU - Ritz, Christian

AU - Hothorn, Ludwig A

N1 - CURIS 2018 NEXS 070

PY - 2018

Y1 - 2018

N2 - Simultaneous inference in longitudinal, repeated-measures, and multi-endpoint designs can be onerous, especially when trying to find a reasonable joint model from which the interesting effects and covariances are estimated. A novel statistical approach known as multiple marginal models greatly simplifies the modelling process: the core idea is to "marginalise" the problem and fit multiple small models to different portions of the data, and then estimate the overall covariance matrix in a subsequent, separate step. Using these estimates guarantees strong control of the family-wise error rate, however only asymptotically. In this paper, we show how to make the approach also applicable to small-sample data problems. Specifically, we discuss the computation of adjusted P values and simultaneous confidence bounds for comparisons of randomised treatment groups as well as for levels of a nonrandomised factor such as multiple endpoints, repeated measures, or a series of points in time or space. We illustrate the practical use of the method with a data example.

AB - Simultaneous inference in longitudinal, repeated-measures, and multi-endpoint designs can be onerous, especially when trying to find a reasonable joint model from which the interesting effects and covariances are estimated. A novel statistical approach known as multiple marginal models greatly simplifies the modelling process: the core idea is to "marginalise" the problem and fit multiple small models to different portions of the data, and then estimate the overall covariance matrix in a subsequent, separate step. Using these estimates guarantees strong control of the family-wise error rate, however only asymptotically. In this paper, we show how to make the approach also applicable to small-sample data problems. Specifically, we discuss the computation of adjusted P values and simultaneous confidence bounds for comparisons of randomised treatment groups as well as for levels of a nonrandomised factor such as multiple endpoints, repeated measures, or a series of points in time or space. We illustrate the practical use of the method with a data example.

KW - Correlated data

KW - Degrees of freedom

KW - Linear mixed-effects model

KW - Multiple contrast test

U2 - 10.1002/sim.7610

DO - 10.1002/sim.7610

M3 - Journal article

VL - 37

SP - 1562

EP - 1576

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 9

ER -

ID: 189867879