TY - JOUR
T1 - Adaptive enrichment trials
T2 - What are the benefits?
AU - Burnett, Thomas
AU - Jennison, Christopher
N1 - Funding Information:
The first author received financial support for this research from the UK Engineering and Physical Sciences Research Council and Hoffman‐LaRoche Ltd. The authors would like to thank to Lucy Rowell for her contributions to this project.
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
UR - http://www.scopus.com/inward/record.url?scp=85096696942&partnerID=8YFLogxK
U2 - 10.1002/sim.8797
DO - 10.1002/sim.8797
M3 - Article
C2 - 33244786
AN - SCOPUS:85096696942
SN - 0277-6715
VL - 40
SP - 690
EP - 711
JO - Statistics in medicine
JF - Statistics in medicine
IS - 3
ER -