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Why are two mistakes not worse than one?: a proposal for controlling the expected number of false claims

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Why are two mistakes not worse than one? a proposal for controlling the expected number of false claims. / Jaki, Thomas Friedrich; Parry, Alice.

In: Pharmaceutical Statistics, Vol. 15, No. 4, 07.2016, p. 362-367.

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@article{a2379a7ae07947ac84256bf674ed5ee1,
title = "Why are two mistakes not worse than one?: a proposal for controlling the expected number of false claims",
abstract = "Multiplicity is common in clinical studies and the current standard is to use the familywise error rate to ensure that the errors are kept at a prespecified level. In this paper, we will show that, in certain situations, familywise error rate control does not account for all errors made. To counteract this problem, we propose the use of the expected number of false claims (EFC). We will show that a (weighted) Bonferroni approach can be used to control the EFC, discuss how a study that uses the EFC can be powered for co-primary, exchangeable, and hierarchical endpoints, and show how the weight for the weighted Bonferroni test can be determined in this manner. ",
keywords = "expected number of false claims (EFC), familywise error rate, hierarchical endpoints, multiplicity",
author = "Jaki, {Thomas Friedrich} and Alice Parry",
note = "{\textcopyright}2016 The Authors. Pharmaceutical Statistics Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.",
year = "2016",
month = jul,
doi = "10.1002/pst.1751",
language = "English",
volume = "15",
pages = "362--367",
journal = "Pharmaceutical Statistics",
issn = "1539-1604",
publisher = "John Wiley and Sons Ltd",
number = "4",

}

RIS

TY - JOUR

T1 - Why are two mistakes not worse than one?

T2 - a proposal for controlling the expected number of false claims

AU - Jaki, Thomas Friedrich

AU - Parry, Alice

N1 - ©2016 The Authors. Pharmaceutical Statistics Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

PY - 2016/7

Y1 - 2016/7

N2 - Multiplicity is common in clinical studies and the current standard is to use the familywise error rate to ensure that the errors are kept at a prespecified level. In this paper, we will show that, in certain situations, familywise error rate control does not account for all errors made. To counteract this problem, we propose the use of the expected number of false claims (EFC). We will show that a (weighted) Bonferroni approach can be used to control the EFC, discuss how a study that uses the EFC can be powered for co-primary, exchangeable, and hierarchical endpoints, and show how the weight for the weighted Bonferroni test can be determined in this manner.

AB - Multiplicity is common in clinical studies and the current standard is to use the familywise error rate to ensure that the errors are kept at a prespecified level. In this paper, we will show that, in certain situations, familywise error rate control does not account for all errors made. To counteract this problem, we propose the use of the expected number of false claims (EFC). We will show that a (weighted) Bonferroni approach can be used to control the EFC, discuss how a study that uses the EFC can be powered for co-primary, exchangeable, and hierarchical endpoints, and show how the weight for the weighted Bonferroni test can be determined in this manner.

KW - expected number of false claims (EFC)

KW - familywise error rate

KW - hierarchical endpoints

KW - multiplicity

U2 - 10.1002/pst.1751

DO - 10.1002/pst.1751

M3 - Journal article

VL - 15

SP - 362

EP - 367

JO - Pharmaceutical Statistics

JF - Pharmaceutical Statistics

SN - 1539-1604

IS - 4

ER -