Simultaneous confidence intervals on multivariate non‐inferiority

Hasler, Mario; Hothorn, Ludwig A.

doi:10.1002/sim.5633

Cited by 9 publications

(11 citation statements)

References 31 publications

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“…Outside the framework of PK analysis, however, there are also applications where equivalence of some but not all endpoints is interesting. The step‐up procedure of Quan et al yields simultaneous CIs for this case, and for one‐sided problems of noninferiority, we refer to Hasler and Hothorn …”

Section: Discussionmentioning

confidence: 99%

Simultaneous confidence regions for multivariate bioequivalence

Pallmann

Jaki

2017

Statistics in Medicine

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Demonstrating bioequivalence of several pharmacokinetic (PK) parameters, such as AUC and C , that are calculated from the same biological sample measurements is in fact a multivariate problem, even though this is neglected by most practitioners and regulatory bodies, who typically settle for separate univariate analyses. We believe, however, that a truly multivariate evaluation of all PK measures simultaneously is clearly more adequate. In this paper, we review methods to construct joint confidence regions around multivariate normal means and investigate their usefulness in simultaneous bioequivalence problems via simulation. Some of them work well for idealised scenarios but break down when faced with real-data challenges such as unknown variance and correlation among the PK parameters. We study the shapes of the confidence regions resulting from different methods, discuss how marginal simultaneous confidence intervals for the individual PK measures can be derived, and illustrate the application to data from a trial on ticlopidine hydrochloride. An R package is available.

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Section: Discussionmentioning

confidence: 99%

Simultaneous confidence regions for multivariate bioequivalence

Pallmann

Jaki

2017

Statistics in Medicine

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“…In other words, testing such an expanded family of hypotheses is as good as testing the family with individual hypotheses only. It is worthwhile to mention that similar UI formulation of composite hypotheses is becoming increasingly common in multi-endpoint bioequivalence and non-inferiority studies of drugs (Quan et al 2001; Logan & Tamhane 2008; Hasler & Hothorn 2013; Hua et al 2015). This stems from the need for inference on individual endpoints.…”

Section: Methodsmentioning

confidence: 99%

“…Such consistency among outcomes was recognized by Dmitrienko et al (2013) as a desirable property. It has served as an incentive for Quan et al (2001) and Hasler & Hothorn (2013) to apply the SH adjustment in clinical equivalence and non-inferiority studies.…”

Section: Methodsmentioning

confidence: 99%

Statistical strategies for multiple testing in the safety evaluation of a genetically modified crop

Vahl

Kang²

2016

J. Agric. Sci.

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Hazard identification is the first step in assessing the risk of a genetically modified (GM) crop. It employs the concept of substantial equivalence to evaluate crop safety. The current process relies on subjective opinions to integrate various comparisons among the GM crop, the non-GM counterpart and an assortment of non-GM references over an array of key endpoints measured in field trials. The pre-eminent need to control the consumer's risk in hazard identification has been left unaddressed. The current paper develops statistical strategies to resolve this issue. Hypotheses of individual tests are explicitly defined to reflect the study objectives. They are then grouped into families and connected by logical operators according to decision rules commonly used in crop safety evaluation. This pre-specification of hypotheses arranged in an organized layout leads to a simple, transparent decision-making process where the consumer's risk can be managed directly. A two-stage multiplicity adjustment procedure is created by applying fundamental principles for multiple testing to the newly assembled families of hypotheses. The practical utility of the proposed procedure is shown in a real-world example. Besides being easy to implement and convey, the proposed statistical strategies accommodate the addition of supportive evidence for safety and allow the nature of the genetic modification to be taken into account.

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“…Hasler and Hothorn [ 5 ] use UI and IU testing principles in their procedures when assessing different approaches to analysing MANI trials with multiple correlated endpoints. They summarise their preferred analysis approaches for various hypothesis structures and outline where adjustment for multiple testing is required.…”

Section: Statistical Considerations For Mani Trialsmentioning

confidence: 99%

Recommendations for designing and analysing multi-arm non-inferiority trials: a review of methodology and current practice

Emmerson

Todd

2021

Trials

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Background and purpose Multi-arm non-inferiority (MANI) trials, here defined as non-inferiority trials with multiple experimental treatment arms, can be useful in situations where several viable treatments exist for a disease area or for testing different dose schedules. To maintain the statistical integrity of such trials, issues regarding both design and analysis must be considered, from both the multi-arm and the non-inferiority perspectives. Little guidance currently exists on exactly how these aspects should be addressed and it is the aim of this paper to provide recommendations to aid the design of future MANI trials. Methods A comprehensive literature review covering four databases was conducted to identify publications associated with MANI trials. Literature was split into methodological and trial publications in order to investigate the required design and analysis considerations for MANI trials and whether they were being addressed in practice. Results A number of issues were identified that if not properly addressed, could lead to issues with the FWER, power or bias. These ranged from the structuring of trial hypotheses at the design stage to the consideration of potential heterogeneous treatment variances at the analysis stage. One key issue of interest was adjustment for multiple testing at the analysis stage. There was little consensus concerning whether more powerful p value adjustment methods were preferred to approximate adjusted CIs when presenting and interpreting the results of MANI trials. We found 65 examples of previous MANI trials, of which 31 adjusted for multiple testing out of the 39 that were adjudged to require it. Trials generally preferred to utilise simple, well-known methods for study design and analysis and while some awareness was shown concerning FWER inflation and choice of power, many trials seemed not to consider the issues and did not provide sufficient definition of their chosen design and analysis approaches. Conclusions While MANI trials to date have shown some awareness of the issues raised within this paper, very few have satisfied the criteria of the outlined recommendations. Going forward, trials should consider the recommendations in this paper and ensure they clearly define and reason their choices of trial design and analysis techniques.

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Simultaneous confidence intervals on multivariate non‐inferiority

Cited by 9 publications

References 31 publications

Simultaneous confidence regions for multivariate bioequivalence

Simultaneous confidence regions for multivariate bioequivalence

Statistical strategies for multiple testing in the safety evaluation of a genetically modified crop

Recommendations for designing and analysing multi-arm non-inferiority trials: a review of methodology and current practice

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