Literature DB >> 10750058

Re-calculating the sample size in internal pilot study designs with control of the type I error rate.

M Kieser1, T Friede.   

Abstract

When designing a clinical trial, there is usually some uncertainty about the variability of the primary outcome variable. This may lead to an unnecessarily high or inadequately low sample size. The internal pilot study approach uses data from patients recruited up to an interim stage to re-estimate the variance and to re-calculate the final sample size accordingly. Previously, simulation studies have shown that this methodology may highly improve the chance to obtain a well-powered trial. However, it also turned out that the type I error rate may be inflated by this procedure. We quantify the maximum excess of the type I error rate for normally distributed outcomes. If strict control of the alpha-level is considered to be an important issue, a method is proposed to achieve this when re-calculating the sample size in internal pilot studies. The characteristics of the power distributions are investigated for various sample size adaptation rules and implications are discussed. Copyright 2000 John Wiley & Sons, Ltd.

Entities:  

Mesh:

Year:  2000        PMID: 10750058     DOI: 10.1002/(sici)1097-0258(20000415)19:7<901::aid-sim405>3.0.co;2-l

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  14 in total

1.  Internal pilots for a class of linear mixed models with Gaussian and compound symmetric data.

Authors:  Matthew J Gurka; Christopher S Coffey; Keith E Muller
Journal:  Stat Med       Date:  2007-09-30       Impact factor: 2.373

2.  Practical Methods for Bounding Type I Error Rate with an Internal Pilot Design.

Authors:  Christopher S Coffey; John A Kairalla; Keith E Muller
Journal:  Commun Stat Theory Methods       Date:  2007       Impact factor: 0.893

3.  GLUMIP 2.0: SAS/IML Software for Planning Internal Pilots.

Authors:  John A Kairalla; Christopher S Coffey; Keith E Muller
Journal:  J Stat Softw       Date:  2008-11-13       Impact factor: 6.440

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

Authors:  Peter Bauer; Frank Bretz; Vladimir Dragalin; Franz König; Gernot Wassmer
Journal:  Stat Med       Date:  2015-03-16       Impact factor: 2.373

5.  Internal pilot design for balanced repeated measures.

Authors:  Xinrui Zhang; Keith E Muller; Maureen M Goodenow; Yueh-Yun Chi
Journal:  Stat Med       Date:  2017-11-21       Impact factor: 2.373

6.  Trial of Labor After Cesarean Section Among Women with Unique Lower Segment Scarred Uterus and Fetal Weight >3500 g: Prognostic Factors for a Safe Vaginal Delivery.

Authors:  Elie Nkwabong; Joseph Nelson Fomulu; Fabrice Lionel Djomkam Youmsi
Journal:  J Obstet Gynaecol India       Date:  2016-03-03

7.  An internal pilot design for prospective cancer screening trials with unknown disease prevalence.

Authors:  John T Brinton; Brandy M Ringham; Deborah H Glueck
Journal:  Trials       Date:  2015-10-13       Impact factor: 2.279

8.  Self-monitoring and psychoeducation in bipolar patients with a smart-phone application (SIMPLe) project: design, development and studies protocols.

Authors:  Diego Hidalgo-Mazzei; Ainoa Mateu; María Reinares; Juan Undurraga; Caterina del Mar Bonnín; José Sánchez-Moreno; Eduard Vieta; Francesc Colom
Journal:  BMC Psychiatry       Date:  2015-03-20       Impact factor: 3.630

9.  A tutorial on pilot studies: the what, why and how.

Authors:  Lehana Thabane; Jinhui Ma; Rong Chu; Ji Cheng; Afisi Ismaila; Lorena P Rios; Reid Robson; Marroon Thabane; Lora Giangregorio; Charles H Goldsmith
Journal:  BMC Med Res Methodol       Date:  2010-01-06       Impact factor: 4.615

Review 10.  Adaptive trial designs: a review of barriers and opportunities.

Authors:  John A Kairalla; Christopher S Coffey; Mitchell A Thomann; Keith E Muller
Journal:  Trials       Date:  2012-08-23       Impact factor: 2.279

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.