Literature DB >> 27019516

Simultaneous confidence intervals that are compatible with closed testing in adaptive designs.

D Magirr1, T Jaki1, M Posch2, F Klinglmueller2.   

Abstract

We describe a general method for finding a confidence region for a parameter vector that is compatible with the decisions of a two-stage closed test procedure in an adaptive experiment. The closed test procedure is characterized by the fact that rejection or nonrejection of a null hypothesis may depend on the decisions for other hypotheses and the compatible confidence region will, in general, have a complex, nonrectangular shape. We find the smallest cross-product of simultaneous confidence intervals containing the region and provide computational shortcuts for calculating the lower bounds on parameters corresponding to the rejected null hypotheses. We illustrate the method with an adaptive phase II/III clinical trial.

Entities:  

Keywords:  Closed testing principle; Combination test; Conditional error; Multiple comparisons; Simultaneous inference

Year:  2013        PMID: 27019516      PMCID: PMC4806862          DOI: 10.1093/biomet/ast035

Source DB:  PubMed          Journal:  Biometrika        ISSN: 0006-3444            Impact factor:   2.445


  9 in total

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5.  Adaptive Dunnett tests for treatment selection.

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7.  Compatible simultaneous lower confidence bounds for the Holm procedure and other Bonferroni-based closed tests.

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8.  Simultaneous confidence regions corresponding to Holm's step-down procedure and other closed-testing procedures.

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Journal:  Biom J       Date:  2008-10       Impact factor: 2.207

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  9 in total
  10 in total

1.  Point and interval estimation in two-stage adaptive designs with time to event data and biomarker-driven subpopulation selection.

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2.  Twenty-five years of confirmatory adaptive designs: opportunities and pitfalls.

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5.  Adaptive designs in clinical trials: why use them, and how to run and report them.

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6.  Optimizing subgroup selection in two-stage adaptive enrichment and umbrella designs.

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8.  Design and estimation in clinical trials with subpopulation selection.

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10.  Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19.

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Journal:  Stat Biopharm Res       Date:  2020-07-29       Impact factor: 1.452

  10 in total

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