Literature DB >> 23509095

Blinded sample size re-estimation in superiority and noninferiority trials: bias versus variance in variance estimation.

Tim Friede1, Meinhard Kieser.   

Abstract

The internal pilot study design allows for modifying the sample size during an ongoing study based on a blinded estimate of the variance thus maintaining the trial integrity. Various blinded sample size re-estimation procedures have been proposed in the literature. We compare the blinded sample size re-estimation procedures based on the one-sample variance of the pooled data with a blinded procedure using the randomization block information with respect to bias and variance of the variance estimators, and the distribution of the resulting sample sizes, power, and actual type I error rate. For reference, sample size re-estimation based on the unblinded variance is also included in the comparison. It is shown that using an unbiased variance estimator (such as the one using the randomization block information) for sample size re-estimation does not guarantee that the desired power is achieved. Moreover, in situations that are common in clinical trials, the variance estimator that employs the randomization block length shows a higher variability than the simple one-sample estimator and in turn the sample size resulting from the related re-estimation procedure. This higher variability can lead to a lower power as was demonstrated in the setting of noninferiority trials. In summary, the one-sample estimator obtained from the pooled data is extremely simple to apply, shows good performance, and is therefore recommended for application.
Copyright © 2013 John Wiley & Sons, Ltd.

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Year:  2013        PMID: 23509095     DOI: 10.1002/pst.1564

Source DB:  PubMed          Journal:  Pharm Stat        ISSN: 1539-1604            Impact factor:   1.894


  7 in total

1.  Sample size requirements to estimate key design parameters from external pilot randomised controlled trials: a simulation study.

Authors:  M Dawn Teare; Munyaradzi Dimairo; Neil Shephard; Alex Hayman; Amy Whitehead; Stephen J Walters
Journal:  Trials       Date:  2014-07-03       Impact factor: 2.279

2.  Blinded versus unblinded estimation of a correlation coefficient to inform interim design adaptations.

Authors:  Cornelia U Kunz; Nigel Stallard; Nicholas Parsons; Susan Todd; Tim Friede
Journal:  Biom J       Date:  2016-11-25       Impact factor: 2.207

3.  Sample size recalculation based on the prevalence in a randomized test-treatment study.

Authors:  Amra Hot; Norbert Benda; Patrick M Bossuyt; Oke Gerke; Werner Vach; Antonia Zapf
Journal:  BMC Med Res Methodol       Date:  2022-07-25       Impact factor: 4.612

Review 4.  Blinding in Clinical Trials: Seeing the Big Picture.

Authors:  Thomas F Monaghan; Christina W Agudelo; Syed N Rahman; Alan J Wein; Jason M Lazar; Karel Everaert; Roger R Dmochowski
Journal:  Medicina (Kaunas)       Date:  2021-06-24       Impact factor: 2.430

5.  Blinded and unblinded sample size reestimation in crossover trials balanced for period.

Authors:  Michael J Grayling; Adrian P Mander; James M S Wason
Journal:  Biom J       Date:  2018-08-03       Impact factor: 2.207

6.  Blinded and unblinded sample size reestimation procedures for stepped-wedge cluster randomized trials.

Authors:  Michael J Grayling; Adrian P Mander; James M S Wason
Journal:  Biom J       Date:  2018-08-03       Impact factor: 2.207

7.  Estimation after blinded sample size reassessment.

Authors:  Martin Posch; Florian Klinglmueller; Franz König; Frank Miller
Journal:  Stat Methods Med Res       Date:  2016-10-02       Impact factor: 3.021

  7 in total

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