Loading [MathJax]/extensions/tex2jax.js

Model-based simultaneous inference for multiple subgroups and multiple endpoints

Publikation: Arbeitspapier/Preprint › Preprint

Autorschaft

Organisationseinheiten

Externe Organisationen

  • Københavns Universitet
  • Medizinische Universität Wien

Details

OriginalspracheEnglisch
PublikationsstatusElektronisch veröffentlicht (E-Pub) - 22 Juli 2020

Abstract

Various methodological options exist on evaluating differences in both subgroups and the overall population. Most desirable is the simultaneous study of multiple endpoints in several populations. We investigate a newer method using multiple marginal models (mmm) which allows flexible handling of multiple endpoints, including continuous, binary or time-to-event data. This paper explores the performance of mmm in contrast to the standard Bonferroni approach via simulation. Mainly these methods are compared on the basis of their familywise error rate and power under different scenarios, varying in sample size and standard deviation. Additionally, it is shown that the method can deal with overlapping subgroup definitions and different combinations of endpoints may be assumed. The reanalysis of a clinical example shows a practical application.

Zitieren

Model-based simultaneous inference for multiple subgroups and multiple endpoints. / Vogel, Charlotte; Schaarschmidt, Frank; Ritz, Christian et al.
2020.

Publikation: Arbeitspapier/Preprint › Preprint

Vogel C, Schaarschmidt F, Ritz C, Koenig F, Hothorn LA. Model-based simultaneous inference for multiple subgroups and multiple endpoints. 2020 Jul 22. Epub 2020 Jul 22. doi: 10.48550/arXiv.2007.11358
Download
@techreport{bdffee2549a64116ae2f2aea0064df02,
title = "Model-based simultaneous inference for multiple subgroups and multiple endpoints",
abstract = " Various methodological options exist on evaluating differences in both subgroups and the overall population. Most desirable is the simultaneous study of multiple endpoints in several populations. We investigate a newer method using multiple marginal models (mmm) which allows flexible handling of multiple endpoints, including continuous, binary or time-to-event data. This paper explores the performance of mmm in contrast to the standard Bonferroni approach via simulation. Mainly these methods are compared on the basis of their familywise error rate and power under different scenarios, varying in sample size and standard deviation. Additionally, it is shown that the method can deal with overlapping subgroup definitions and different combinations of endpoints may be assumed. The reanalysis of a clinical example shows a practical application. ",
keywords = "stat.AP",
author = "Charlotte Vogel and Frank Schaarschmidt and Christian Ritz and Franz Koenig and Hothorn, {Ludwig A.}",
year = "2020",
month = jul,
day = "22",
doi = "10.48550/arXiv.2007.11358",
language = "English",
type = "WorkingPaper",

}

Download

TY - UNPB

T1 - Model-based simultaneous inference for multiple subgroups and multiple endpoints

AU - Vogel, Charlotte

AU - Schaarschmidt, Frank

AU - Ritz, Christian

AU - Koenig, Franz

AU - Hothorn, Ludwig A.

PY - 2020/7/22

Y1 - 2020/7/22

N2 - Various methodological options exist on evaluating differences in both subgroups and the overall population. Most desirable is the simultaneous study of multiple endpoints in several populations. We investigate a newer method using multiple marginal models (mmm) which allows flexible handling of multiple endpoints, including continuous, binary or time-to-event data. This paper explores the performance of mmm in contrast to the standard Bonferroni approach via simulation. Mainly these methods are compared on the basis of their familywise error rate and power under different scenarios, varying in sample size and standard deviation. Additionally, it is shown that the method can deal with overlapping subgroup definitions and different combinations of endpoints may be assumed. The reanalysis of a clinical example shows a practical application.

AB - Various methodological options exist on evaluating differences in both subgroups and the overall population. Most desirable is the simultaneous study of multiple endpoints in several populations. We investigate a newer method using multiple marginal models (mmm) which allows flexible handling of multiple endpoints, including continuous, binary or time-to-event data. This paper explores the performance of mmm in contrast to the standard Bonferroni approach via simulation. Mainly these methods are compared on the basis of their familywise error rate and power under different scenarios, varying in sample size and standard deviation. Additionally, it is shown that the method can deal with overlapping subgroup definitions and different combinations of endpoints may be assumed. The reanalysis of a clinical example shows a practical application.

KW - stat.AP

U2 - 10.48550/arXiv.2007.11358

DO - 10.48550/arXiv.2007.11358

M3 - Preprint

BT - Model-based simultaneous inference for multiple subgroups and multiple endpoints

ER -

Von denselben Autoren