Group Sequential and Confirmatory Adaptive Designs in Clinical Trials

Wassmer, Gernot; Brannath, Werner

doi:10.1007/978-3-319-32562-0

Cited by 124 publications

(173 citation statements)

References 272 publications

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“…One can also incorporate early rejection by pre‐specifying α 1 < α and rejecting the null at the interim analysis if p 1 ≤ α 1 . The critical values α 1 and c are chosen such that p 1 ≤ α 1 or C ( p 1 , p 2 ) ≤ c with probability α for stochastically independent and uniformly distributed p 1 , p 2 ; we can also pre‐specify a binding early acceptance bound and account for it in the calculation of α 1 and/or c . Since a binding futility bound is rarely implemented in practice, we will not consider this option here.…”

Section: Nonparametric Combination Tests Based On Weighted Km Estimatesmentioning

confidence: 99%

“…The latter method is also denoted as weighted z score test and has the advantage that for α 1 = 0 (no early rejection), we obtain c = α , because C ( p 1 , p 2 ) is uniformly distributed when p 1 and p 2 are independent and uniformly distributed.. With early rejection, we can use the local rejection levels of a group sequential design, because Z 1 = Φ −1 (1 − p 1 ) and Z 2 = w 1 Φ −1 (1 − p 1 ) + w 2 Φ −1 (1 − p 2 ) have the same common distribution as the z scores in a group sequential two‐stage test with information times

w_{1}^{2}

and 1. The combination test methodology can easily be extended to adaptive designs with more than a single interim analysis …”

Section: Nonparametric Combination Tests Based On Weighted Km Estimatesmentioning

confidence: 99%

“…Adaptive designs are a research topic for more than 25 years and are still a vital field with interesting and challenging questions . One of the yet insufficiently resolved issues is type I error rate control in adaptive survival trials when data from surrogate endpoints are used .…”

Section: Introductionmentioning

confidence: 99%

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Nonparametric adaptive enrichment designs using categorical surrogate data

Brückner

Burger

Brannath

2018

Statistics in Medicine

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Adaptive survival trials are particularly important for enrichment designs in oncology and other life-threatening diseases. Current statistical methodology for adaptive survival trials provide type I error rate control only under restrictions. For instance, if we use stage-wise P values based on increments of the log-rank test, then the information used for the interim decisions need to be restricted to the primary survival endpoint. However, it is often desirable to base interim decisions also on correlated short-term endpoints like tumor response. Alternative statistical approaches based on a patient-wise splitting of the data require unnatural restrictions on the follow-up times and do not permit to efficiently account for an early rejection of the primary null hypothesis. We therefore suggest new approaches that enable us to use discrete surrogate endpoints (like tumor response status) and also to incorporate interim rejection boundaries. The new approaches are based on weighted Kaplan-Meier estimates and thereby have additional advantages. They permit us to account for nonproportional hazards and are robust against informative censoring based on the surrogate endpoint. We will show that nonproportionality is an intrinsic and relevant issue in enrichment designs. Moreover, informative censoring based on the surrogate endpoint is likely because of withdrawals and treatment switches after insufficient treatment response. It is shown and illustrated how nonparametric tests based on weighted Kaplan-Meier estimates can be used in closed combination tests for adaptive enrichment designs, such that type I error rate control is achieved and justified asymptotically.

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Section: Nonparametric Combination Tests Based On Weighted Km Estimatesmentioning

confidence: 99%

w_{1}^{2}

and 1. The combination test methodology can easily be extended to adaptive designs with more than a single interim analysis …”

Section: Nonparametric Combination Tests Based On Weighted Km Estimatesmentioning

confidence: 99%

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Nonparametric adaptive enrichment designs using categorical surrogate data

Brückner

Burger

Brannath

2018

Statistics in Medicine

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“…Planning a study design as a group sequential design requires considerations before starting the study (see [17]; [18]):…”

Section: Applications Of Sequential Designsmentioning

confidence: 99%

Increasing efficiency of preclinical research by group sequential designs

et al. 2017

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Despite the potential benefits of sequential designs, studies evaluating treatments or experimental manipulations in preclinical experimental biomedicine almost exclusively use classical block designs. Our aim with this article is to bring the existing methodology of group sequential designs to the attention of researchers in the preclinical field and to clearly illustrate its potential utility. Group sequential designs can offer higher efficiency than traditional methods and are increasingly used in clinical trials. Using simulation of data, we demonstrate that group sequential designs have the potential to improve the efficiency of experimental studies, even when sample sizes are very small, as is currently prevalent in preclinical experimental biomedicine. When simulating data with a large effect size of d = 1 and a sample size of n = 18 per group, sequential frequentist analysis consumes in the long run only around 80% of the planned number of experimental units. In larger trials (n = 36 per group), additional stopping rules for futility lead to the saving of resources of up to 30% compared to block designs. We argue that these savings should be invested to increase sample sizes and hence power, since the currently underpowered experiments in preclinical biomedicine are a major threat to the value and predictiveness in this research domain.

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“…という．上の生物学的同等性試験の例で言えば，2 つの片側帰無 Bauer, P., Bretz, F., Dragalin, V., Koenig, F., Wassmer, G. (2016). Twenty-five years of confirmatory adaptive designs: opportunities and pitfalls.…”

mentioning

confidence: 99%

Gatekeeping Strategies and Graphical Approaches in Clinical Trials with Hierarchically Structured Study Objectives: A Review

Sugitani

Morikawa

2017

Jpn J Biomet

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In this paper, we give a review of recent development on gatekeeping strategies and graphical approaches. First, we give an overview of fundamental theories for multiple testing, such as partition testing and closed testing procedure. We then describe how gatekeeping strategies have been developed in the last decade to handle multiplicity issues that arise in clinical trials with hierarchically structured study objectives and how graphical approaches help to visualize such gatekeeping strategies and tailor a multiple testing procedure. Finally, we describe further availability of graphical approaches to more complex clinical trial designs such as group-sequential designs and adaptive designs.

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Group Sequential and Confirmatory Adaptive Designs in Clinical Trials

Cited by 124 publications

References 272 publications

Nonparametric adaptive enrichment designs using categorical surrogate data

Nonparametric adaptive enrichment designs using categorical surrogate data

Increasing efficiency of preclinical research by group sequential designs

Gatekeeping Strategies and Graphical Approaches in Clinical Trials with Hierarchically Structured Study Objectives: A Review

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