Literature DB >> 32627859

Information fraction estimation based on the number of events within the standard treatment regimen.

Ha M Dang1,2, Todd Alonzo1,2, Meredith Franklin1, Wendy J Mack1, Mark D Krailo1,2, Sandrah P Eckel1.   

Abstract

For a Phase III randomized trial that compares survival outcomes between an experimental treatment versus a standard therapy, interim monitoring analysis is used to potentially terminate the study early based on efficacy. To preserve the nominal Type I error rate, alpha spending methods and information fractions are used to compute appropriate rejection boundaries in studies with planned interim analyses. For a one-sided trial design applied to a scenario in which the experimental therapy is superior to the standard therapy, interim monitoring should provide the opportunity to stop the trial prior to full follow-up and conclude that the experimental therapy is superior. This paper proposes a method called total control only (TCO) for estimating the information fraction based on the number of events within the standard treatment regimen. Based on theoretical derivations and simulation studies, for a maximum duration superiority design, the TCO method is not influenced by departure from the designed hazard ratio, is sensitive to detecting treatment differences, and preserves the Type I error rate compared to information fraction estimation methods that are based on total observed events. The TCO method is simple to apply, provides unbiased estimates of the information fraction, and does not rely on statistical assumptions that are impossible to verify at the design stage. For these reasons, the TCO method is a good approach when designing a maximum duration superiority trial with planned interim monitoring analyses.
© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  group sequential analysis; information fraction; interim monitoring analysis; maximum duration clinical trials; pediatric oncology; survival outcome trials

Year:  2020        PMID: 32627859      PMCID: PMC7953992          DOI: 10.1002/bimj.201900236

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


  7 in total

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Authors:  K Kim; H Boucher; A A Tsiatis
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Authors:  James R Anderson; Robin High
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Authors:  K Kim; A A Tsiatis
Journal:  Biometrics       Date:  1990-03       Impact factor: 2.571

4.  Information time scales for interim analyses of randomized clinical trials.

Authors:  Boris Freidlin; Megan Othus; Edward L Korn
Journal:  Clin Trials       Date:  2016-05-02       Impact factor: 2.486

5.  Group sequential procedures: calendar versus information time.

Authors:  D L Demets
Journal:  Stat Med       Date:  1989-10       Impact factor: 2.373

6.  Interim analysis: the alpha spending function approach.

Authors:  D L DeMets; K K Lan
Journal:  Stat Med       Date:  1994 Jul 15-30       Impact factor: 2.373

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Authors:  K K Lan; J M Lachin
Journal:  Biometrics       Date:  1990-09       Impact factor: 2.571

  7 in total
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1.  Combining factorial and multi-arm multi-stage platform designs to evaluate multiple interventions efficiently.

Authors:  Ian R White; Babak Choodari-Oskooei; Matthew R Sydes; Brennan C Kahan; Leanne McCabe; Anna Turkova; Hanif Esmail; Diana M Gibb; Deborah Ford
Journal:  Clin Trials       Date:  2022-05-17       Impact factor: 2.599

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