2013
DOI: 10.1002/sim.5990
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Traditional multiplicity adjustment methods in clinical trials

Abstract: This tutorial discusses important statistical problems arising in clinical trials with multiple clinical objectives based on different clinical variables, evaluation of several doses or regiments of a new treatment, analysis of multiple patient subgroups, etc. Simultaneous assessment of several objectives in a single trial gives rise to multiplicity. If unaddressed, problems of multiplicity can undermine integrity of statistical inferences. The tutorial reviews key concepts in multiple hypothesis testing and i… Show more

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Cited by 116 publications
(89 citation statements)
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References 68 publications
(87 reference statements)
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“…Meningioma subtypes ( n  = 175, subtypes with 1–2 cases were excluded from analysis) and tumor localization ( n  = 185, one intraventricular tumor was excluded from analysis) were compared using the Kruskal–Wallis test. When the Kruskal-Wallis test was significant, pairwise comparisons between groups were performed using Dunn’s test and the Hommel adjustment (Dmitrienko & D’Agostino, 2013, page 5191) for multiple comparisons and to preserve the familywise error rate. Cox regression was used in both univariable and multivariable survival analyses based on continuous SI-values.…”
Section: Methodsmentioning
confidence: 99%
“…Meningioma subtypes ( n  = 175, subtypes with 1–2 cases were excluded from analysis) and tumor localization ( n  = 185, one intraventricular tumor was excluded from analysis) were compared using the Kruskal–Wallis test. When the Kruskal-Wallis test was significant, pairwise comparisons between groups were performed using Dunn’s test and the Hommel adjustment (Dmitrienko & D’Agostino, 2013, page 5191) for multiple comparisons and to preserve the familywise error rate. Cox regression was used in both univariable and multivariable survival analyses based on continuous SI-values.…”
Section: Methodsmentioning
confidence: 99%
“…The randomization algorithm is implemented using statistical package SAS 9.2 and modified according to Dmitrienko et al [32]. …”
Section: Methods and Designmentioning
confidence: 99%
“…Here the type 1 error rate is kept under α because this is an example of an intersection-union test (Dmitrienko and D'Agostino, 2013). Then, at the approximate confidence level of (1 − α), we can conclude that…”
Section: Approximate Linearized Inequalitiesmentioning
confidence: 99%