Literature DB >> 16053251

Drop-the-losers design: normal case.

Allan R Sampson1, Michael W Sill.   

Abstract

Drop-the-losers designs are statistical designs which have two stages of a trial separated by a data based decision. In the first stage k experimental treatments and a control are administered. During a transition period, the empirically best experimental treatment is selected for continuation into the second phase, along with the control. At the study's end, inference focuses on the comparison of the selected treatment with the control using both stages' data. Traditional methods used to make inferences based on both stages' data can yield tests with higher than advertised levels of significance and confidence intervals with lower than advertised confidence. For normally distributed data, methods are provided to correct these deficiencies, providing confidence intervals with accurate levels of confidence. Drop-the-losers designs are particularly applicable to biopharmaceutical clinical trials where they can allow Phase II and Phase III clinical trials to be conducted under a single protocol with the use of all available data.

Mesh:

Year:  2005        PMID: 16053251     DOI: 10.1002/bimj.200410119

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


  20 in total

1.  Optimizing randomized trial designs to distinguish which subpopulations benefit from treatment.

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2.  Rethinking Phase II Clinical Trial Design in Heart Failure.

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3.  Response adaptive randomization procedures in seamless phase II/III clinical trials.

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4.  Estimation of treatment effect following a clinical trial with adaptive design.

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5.  Accounting for selection and correlation in the analysis of two-stage genome-wide association studies.

Authors:  David S Robertson; A Toby Prevost; Jack Bowden
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6.  Confidence intervals for the selected population in randomized trials that adapt the population enrolled.

Authors:  Michael Rosenblum
Journal:  Biom J       Date:  2013-04-03       Impact factor: 2.207

7.  Drop-the-Losers Design: Binomial Case.

Authors:  Michael W Sill; Allan R Sampson
Journal:  Comput Stat Data Anal       Date:  2009-01-01       Impact factor: 1.681

8.  Unbiased estimation of odds ratios: combining genomewide association scans with replication studies.

Authors:  Jack Bowden; Frank Dudbridge
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9.  A multi-stage drop-the-losers design for multi-arm clinical trials.

Authors:  James Wason; Nigel Stallard; Jack Bowden; Christopher Jennison
Journal:  Stat Methods Med Res       Date:  2016-09-30       Impact factor: 3.021

Review 10.  Experimental designs for small randomised clinical trials: an algorithm for choice.

Authors:  Catherine Cornu; Behrouz Kassai; Roland Fisch; Catherine Chiron; Corinne Alberti; Renzo Guerrini; Anna Rosati; Gerard Pons; Harm Tiddens; Sylvie Chabaud; Daan Caudri; Clément Ballot; Polina Kurbatova; Anne-Charlotte Castellan; Agathe Bajard; Patrice Nony; Leon Aarons; Agathe Bajard; Clément Ballot; Yves Bertrand; Frank Bretz; Daan Caudri; Charlotte Castellan; Sylvie Chabaud; Catherine Cornu; Frank Dufour; Cornelia Dunger-Baldauf; Jean-Marc Dupont; Roland Fisch; Renzo Guerrini; Vincent Jullien; Behrouz Kassaï; Patrice Nony; Kayode Ogungbenro; David Pérol; Gérard Pons; Harm Tiddens; Anna Rosati; Corinne Alberti; Catherine Chiron; Polina Kurbatova; Rima Nabbout
Journal:  Orphanet J Rare Dis       Date:  2013-03-25       Impact factor: 4.123

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