Literature DB >> 20047003

Drop-the-Losers Design: Binomial Case.

Michael W Sill1, Allan R Sampson.   

Abstract

Drop-the-losers designs were introduced for normal distributions as a method of combining phase II and III clinical trials together under a single protocol with the purpose of more rapidly evaluating drugs by eliminating as much as possible the delays that typically occur between the two phases of clinical development. In the design, the sponsor would administer k treatments along with a control in the first stage. During a brief interim period, efficacy data would be used to select the best treatment (with a rule to deal with ties) for further evaluation against the control in a second stage. At the end of the study, data from both stages would be used to draw inferences about the selected treatment relative to the control with adjustments made for selection in between the two stages. Because the inferences are model based, exact confidence intervals can be determined for the parameter of interest. In the present case, the parameter of concern is the probability of a beneficial response that is dichotomous in nature.

Entities:  

Year:  2009        PMID: 20047003      PMCID: PMC2654329          DOI: 10.1016/j.csda.2008.07.031

Source DB:  PubMed          Journal:  Comput Stat Data Anal        ISSN: 0167-9473            Impact factor:   1.681


  3 in total

1.  Combining different phases in the development of medical treatments within a single trial.

Authors:  P Bauer; M Kieser
Journal:  Stat Med       Date:  1999-07-30       Impact factor: 2.373

Review 2.  Likelihood methods for measuring statistical evidence.

Authors:  Jeffrey D Blume
Journal:  Stat Med       Date:  2002-09-15       Impact factor: 2.373

3.  Drop-the-losers design: normal case.

Authors:  Allan R Sampson; Michael W Sill
Journal:  Biom J       Date:  2005-06       Impact factor: 2.207

  3 in total
  2 in total

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

Authors:  Xiaolong Luo; Mingyu Li; Weichung Joe Shih; Peter Ouyang
Journal:  J Biopharm Stat       Date:  2012       Impact factor: 1.051

2.  Correcting for bias in the selection and validation of informative diagnostic tests.

Authors:  David S Robertson; A Toby Prevost; Jack Bowden
Journal:  Stat Med       Date:  2015-02-01       Impact factor: 2.373

  2 in total

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