Literature DB >> 24173686

A comparison of methods for constructing confidence intervals after phase II/III clinical trials.

Peter K Kimani1, Susan Todd, Nigel Stallard.   

Abstract

Recently, in order to accelerate drug development, trials that use adaptive seamless designs such as phase II/III clinical trials have been proposed. Phase II/III clinical trials combine traditional phases II and III into a single trial that is conducted in two stages. Using stage 1 data, an interim analysis is performed to answer phase II objectives and after collection of stage 2 data, a final confirmatory analysis is performed to answer phase III objectives. In this paper we consider phase II/III clinical trials in which, at stage 1, several experimental treatments are compared to a control and the apparently most effective experimental treatment is selected to continue to stage 2. Although these trials are attractive because the confirmatory analysis includes phase II data from stage 1, the inference methods used for trials that compare a single experimental treatment to a control and do not have an interim analysis are no longer appropriate. Several methods for analysing phase II/III clinical trials have been developed. These methods are recent and so there is little literature on extensive comparisons of their characteristics. In this paper we review and compare the various methods available for constructing confidence intervals after phase II/III clinical trials.
© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  Adaptive seamless designs; Confidence intervals; Estimation; Multi-arm multi-stage trials; Treatment selection

Mesh:

Year:  2013        PMID: 24173686     DOI: 10.1002/bimj.201300036

Source DB:  PubMed          Journal:  Biom J        ISSN: 0323-3847            Impact factor:   2.207


  6 in total

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

Authors:  Peter K Kimani; Susan Todd; Lindsay A Renfro; Ekkehard Glimm; Josephine N Khan; John A Kairalla; Nigel Stallard
Journal:  Stat Med       Date:  2020-05-03       Impact factor: 2.373

2.  To add or not to add a new treatment arm to a multiarm study: A decision-theoretic framework.

Authors:  Kim May Lee; James Wason; Nigel Stallard
Journal:  Stat Med       Date:  2019-05-21       Impact factor: 2.373

3.  Estimation after subpopulation selection in adaptive seamless trials.

Authors:  Peter K Kimani; Susan Todd; Nigel Stallard
Journal:  Stat Med       Date:  2015-04-22       Impact factor: 2.373

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

Authors:  Philip Pallmann; Alun W Bedding; Babak Choodari-Oskooei; Munyaradzi Dimairo; Laura Flight; Lisa V Hampson; Jane Holmes; Adrian P Mander; Lang'o Odondi; Matthew R Sydes; Sofía S Villar; James M S Wason; Christopher J Weir; Graham M Wheeler; Christina Yap; Thomas Jaki
Journal:  BMC Med       Date:  2018-02-28       Impact factor: 8.775

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

Authors:  Yi-Da Chiu; Franz Koenig; Martin Posch; Thomas Jaki
Journal:  Stat Med       Date:  2018-08-07       Impact factor: 2.373

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

Authors:  Nigel Stallard; Lisa Hampson; Norbert Benda; Werner Brannath; Thomas Burnett; Tim Friede; Peter K Kimani; Franz Koenig; Johannes Krisam; Pavel Mozgunov; Martin Posch; James Wason; Gernot Wassmer; John Whitehead; S Faye Williamson; Sarah Zohar; Thomas Jaki
Journal:  Stat Biopharm Res       Date:  2020-07-29       Impact factor: 1.452

  6 in total

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