On familywise type I error control for multiplicity in equivalence trials with three or more treatments

Röhmel, Joachim

doi:10.1002/bimj.201100073

Cited by 5 publications

(11 citation statements)

References 20 publications

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“…The WC scenario yields the divisor for the multiple comparisons procedure. We show that the WC scenario involves K 2 /4 comparisons, as is conjectured in Röhmel (2011).…”

Section: Introductionsupporting

confidence: 70%

“…The results of Röhmel (2011) show that arguments from closed testing procedures yields the same corrections, yet demonstrate exact control of the FWER. This exact correction has been proven for K = 3 and 4 could be extended to K = ∞ by analogous use of the arguments.…”

Section: Discussionmentioning

confidence: 76%

“…The procedure only controls the family-wise error rate (FWER) under settings equivalent to when the null hypothesis is true for all pairs of means. Röhmel (2011) suggested that the procedure should control the error rate regardless of the configurations of null or alternative status for the collection of means. Here, we give the appropriate correction for all numbers of groups (K) using the logic from the original article.…”

Section: Introductionmentioning

confidence: 99%

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Correction to “Easy Multiplicity Control in Equivalence Testing Using Two One-Sided Tests”

Caffo

Lauzon

Röhmel

2013

The American Statistician

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“…The WC scenario yields the divisor for the multiple comparisons procedure. We show that the WC scenario involves K 2 /4 comparisons, as is conjectured in Röhmel (2011).…”

Section: Introductionsupporting

confidence: 70%

Section: Discussionmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Correction to “Easy Multiplicity Control in Equivalence Testing Using Two One-Sided Tests”

Caffo

Lauzon

Röhmel

2013

The American Statistician

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“…2015, 34 215-231217 Intersection null hypotheses for the closure test procedure. Legend: Δμ -difference in ln(AUC), Δvdifference in ln(Cmax), = ln(1.25); intersection hypotheses can be found in(4).…”

mentioning

confidence: 49%

“…It should be noted that the eight non-empty sets in Appendix A are the four null sets in (3) plus the last four sets in (4). However, because, for example, H A,01 = (H A,01 ∩H C,01 )∪(H A,01 ∩H C,02 )∪(H A,01 ∩H C,a ), it suffices to test H A,01 ∩H C,a instead of H A,01 as the other two components are already part of the intersection null hypotheses in (4). So the closed test applied in the situation of simultaneous AUC and Cmax BE is actually to test the eight mutually exclusive null hypotheses in (4) each at to allow the control of FWER at the same .…”

Section: Closed Test Procedures In Bioequivalence Using Two One-sided mentioning

confidence: 99%

Multiplicity adjustments in testing for bioequivalence

Hua

D’Agostino

2014

Statistics in Medicine

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Bioequivalence of two drugs is usually demonstrated by rejecting two one-sided null hypotheses using the two one-sided tests for pharmacokinetic parameters: area under the concentration-time curve (AUC) and maximum concentration (Cmax). By virtue of the intersection-union test, there is no need for multiplicity adjustment in testing the two one-sided null hypotheses within each parameter. However, the decision rule for bioequivalence often requires equivalence to be achieved simultaneously on both parameters that contain four one-sided null hypotheses together; without adjusting for multiplicity, the family wise error rate (FWER) could fail to be controlled at the nominal type-I error rate α. The multiplicity issue for bioequivalence in this regard is scarcely discussed in the literature. To address this issue, we propose two approaches including a closed test procedure that controls FWER for the simultaneous AUC and Cmax bioequivalence and requires no adjustment of the type-I error, and an alpha-adaptive sequential testing (AAST) that controls FWER by pre-specifying the significance level on AUC (α1) and obtaining it for Cmax (α2) adaptively after testing of AUC. While both methods control FWER, the closed test requires testing of eight intersection null hypotheses each at α, and AAST is at times accomplished through a slight deduction in α1 and no deduction in α2 relative to α. The latter considers equivalence reached in AUC a higher importance than that in Cmax. Illustrated with published data, the two approaches, although operate differently, can lead to the same substantive conclusion and are better than a traditional method like Bonferroni adjustment.

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Multiplicity issues in clinical trials

Benda

Bender

2011

Biometrical J

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The three papers presented in this special issue on multiplicity issues in clinical trials are based on a workshop that was held at the Federal Institute for Drugs and Medical Devices (BfArM). The workshop was organized to discuss new methods and emerging issues related to multiple testing procedures between industry statisticians, regulators and academia.Multiple testing problems arise in the context of clinical trials from a couple of different sources. Multiple treatment comparisons due to, e.g. different dosages or regimens, multiple endpoints, multiple interim looks, multiple populations or even combinations of these can cause an inflation of the familywise type 1 error. The consequence is an increased probability of wrong decisions in the contexts of drug approval and reimbursement if the analysis strategy does not account properly for multiplicity.In 2002, the EMA Points to Consider on Multiplicity Issues in Clinical Trials came into operation (EMEA CPMP, 2002). Since then, it has been proven to be useful for both, industry and regulators when planning and assessing confirmatory clinical trials. Meanwhile, however, methodological advances have been made in more complex multiplicity settings resulting in more powerful and flexible procedures in situations where several hypotheses are to be tested, eventually with different degrees of prominence attributed to the individual questions. In line with the development of these methods there is an increasing complexity of the primary and secondary hypothesis framework in confirmatory clinical trials. On the one hand, the applicant wishes to answer a number of questions within a single trial that potentially help to improve and accelerate clinical development. On the other hand, it may be in regulator's interest to ensure statistical evidence in different claims to be made with a clinical trial.Some powerful methods have recently been developed, e.g. the different gatekeeping and fallback procedure as well as elegant graphical solutions. The increasing complexity of hypothesis frameworks and methods, however, results in new issues and poses questions on general principles that have not been considered so far. These include consistency problems or the construction of simultaneous confidence intervals.While the consideration of multiplicity issues in confirmatory clinical trials represents an accepted international standard, there is a persisting discussion about the relevance of multiple comparison procedures in other areas of medical research such as epidemiology (Rothman, 1990;Bender and Lange, 2001). In systematic reviews and meta-analyses the general problem of multiplicity has been more or less neglected for decades. However, the recent work of the Statistical Methods Group of Cochrane Collaboration and the Institute for Quality and Efficiency in Health Care led to the inclusion of key recommendations regarding the multiplicity problem in the new version of the Cochrane Handbook (Higgins et al., 2008;Bender, 2009).

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On familywise type I error control for multiplicity in equivalence trials with three or more treatments

Cited by 5 publications

References 20 publications

Correction to “Easy Multiplicity Control in Equivalence Testing Using Two One-Sided Tests”

Correction to “Easy Multiplicity Control in Equivalence Testing Using Two One-Sided Tests”

Multiplicity adjustments in testing for bioequivalence

Multiplicity issues in clinical trials

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