Literature DB >> 4027320

Designing phase II studies in the context of a programme of clinical research.

J Whitehead.   

Abstract

Conventional statistical determinations of sample size in phase II studies typically lead to sample sizes of the order of 25 (Schoenfeld, 1980, International Journal of Radiation Oncology, Biology and Physics 6, 371-374). When the development of new treatments is proceeding rapidly relative to the recruitment of suitable patients, such requirements can prove to be too demanding. As a result, either sample sizes are reduced by a rather arbitrary weakening of the risk specifications, or certain new treatments go untested. In this paper, the phase II testing of a number of treatments will be considered as a single study which has the objective of identifying the most promising treatment for phase III investigation. It is seen to be advantageous to test more treatments, with fewer subjects receiving each, than the conventional methods would allow.

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Year:  1985        PMID: 4027320

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  6 in total

Review 1.  Stopping rules for phase II studies.

Authors:  N Stallard; J Whitehead; S Todd; A Whitehead
Journal:  Br J Clin Pharmacol       Date:  2001-06       Impact factor: 4.335

2.  Controlled multi-arm platform design using predictive probability.

Authors:  Brian P Hobbs; Nan Chen; J Jack Lee
Journal:  Stat Methods Med Res       Date:  2016-01-12       Impact factor: 3.021

Review 3.  Basic design considerations for clinical trials in oncology.

Authors:  S Piantadosi; N Saijo; T Tamura
Journal:  Jpn J Cancer Res       Date:  1992-06

4.  Trial design for evaluating novel treatments during an outbreak of an infectious disease.

Authors:  John Whitehead; Piero Olliaro; Trudie Lang; Peter Horby
Journal:  Clin Trials       Date:  2016-01-14       Impact factor: 2.486

5.  Planning multi-arm screening studies within the context of a drug development program.

Authors:  James M S Wason; Thomas Jaki; Nigel Stallard
Journal:  Stat Med       Date:  2013-03-26       Impact factor: 2.373

6.  Bayesian dose selection design for a binary outcome using restricted response adaptive randomization.

Authors:  Caitlyn Meinzer; Renee Martin; Jose I Suarez
Journal:  Trials       Date:  2017-09-08       Impact factor: 2.279

  6 in total

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