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Adaptive enrichment trials: What are the benefits?

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Adaptive enrichment trials: What are the benefits? / Burnett, T.; Jennison, C.
In: Statistics in Medicine, Vol. 40, No. 3, 10.02.2021, p. 690-711.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Burnett, T & Jennison, C 2021, 'Adaptive enrichment trials: What are the benefits?', Statistics in Medicine, vol. 40, no. 3, pp. 690-711. https://doi.org/10.1002/sim.8797

APA

Burnett, T., & Jennison, C. (2021). Adaptive enrichment trials: What are the benefits? Statistics in Medicine, 40(3), 690-711. https://doi.org/10.1002/sim.8797

Vancouver

Burnett T, Jennison C. Adaptive enrichment trials: What are the benefits? Statistics in Medicine. 2021 Feb 10;40(3):690-711. Epub 2020 Nov 26. doi: 10.1002/sim.8797

Author

Burnett, T. ; Jennison, C. / Adaptive enrichment trials : What are the benefits?. In: Statistics in Medicine. 2021 ; Vol. 40, No. 3. pp. 690-711.

Bibtex

@article{286a779348aa4c3ca3036f87c8cb4fa0,
title = "Adaptive enrichment trials: What are the benefits?",
abstract = "When planning a Phase III clinical trial, suppose a certain subset of patients is expected to respond particularly well to the new treatment. Adaptive enrichment designs make use of interim data in selecting the target population for the remainder of the trial, either continuing with the full population or restricting recruitment to the subset of patients. We define a multiple testing procedure that maintains strong control of the familywise error rate, while allowing for the adaptive sampling procedure. We derive the Bayes optimal rule for deciding whether or not to restrict recruitment to the subset after the interim analysis and present an efficient algorithm to facilitate simulation-based optimisation, enabling the construction of Bayes optimal rules in a wide variety of problem formulations. We compare adaptive enrichment designs with traditional nonadaptive designs in a broad range of examples and draw clear conclusions about the potential benefits of adaptive enrichment. ",
keywords = "adaptive designs, adaptive enrichment, Bayesian optimization, phase III clinical trial, population enrichment, adult, algorithm, article, controlled study, human, phase 3 clinical trial, simulation",
author = "T. Burnett and C. Jennison",
year = "2021",
month = feb,
day = "10",
doi = "10.1002/sim.8797",
language = "English",
volume = "40",
pages = "690--711",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "3",

}

RIS

TY - JOUR

T1 - Adaptive enrichment trials

T2 - What are the benefits?

AU - Burnett, T.

AU - Jennison, C.

PY - 2021/2/10

Y1 - 2021/2/10

N2 - When planning a Phase III clinical trial, suppose a certain subset of patients is expected to respond particularly well to the new treatment. Adaptive enrichment designs make use of interim data in selecting the target population for the remainder of the trial, either continuing with the full population or restricting recruitment to the subset of patients. We define a multiple testing procedure that maintains strong control of the familywise error rate, while allowing for the adaptive sampling procedure. We derive the Bayes optimal rule for deciding whether or not to restrict recruitment to the subset after the interim analysis and present an efficient algorithm to facilitate simulation-based optimisation, enabling the construction of Bayes optimal rules in a wide variety of problem formulations. We compare adaptive enrichment designs with traditional nonadaptive designs in a broad range of examples and draw clear conclusions about the potential benefits of adaptive enrichment. 

AB - When planning a Phase III clinical trial, suppose a certain subset of patients is expected to respond particularly well to the new treatment. Adaptive enrichment designs make use of interim data in selecting the target population for the remainder of the trial, either continuing with the full population or restricting recruitment to the subset of patients. We define a multiple testing procedure that maintains strong control of the familywise error rate, while allowing for the adaptive sampling procedure. We derive the Bayes optimal rule for deciding whether or not to restrict recruitment to the subset after the interim analysis and present an efficient algorithm to facilitate simulation-based optimisation, enabling the construction of Bayes optimal rules in a wide variety of problem formulations. We compare adaptive enrichment designs with traditional nonadaptive designs in a broad range of examples and draw clear conclusions about the potential benefits of adaptive enrichment. 

KW - adaptive designs

KW - adaptive enrichment

KW - Bayesian optimization

KW - phase III clinical trial

KW - population enrichment

KW - adult

KW - algorithm

KW - article

KW - controlled study

KW - human

KW - phase 3 clinical trial

KW - simulation

U2 - 10.1002/sim.8797

DO - 10.1002/sim.8797

M3 - Journal article

VL - 40

SP - 690

EP - 711

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 3

ER -