Improving adaptive seamless designs through Bayesian optimization.
Bayesian optimization
adaptive seamless designs
clinical trials
treatment selection
Journal
Biometrical journal. Biometrische Zeitschrift
ISSN: 1521-4036
Titre abrégé: Biom J
Pays: Germany
ID NLM: 7708048
Informations de publication
Date de publication:
06 2022
06 2022
Historique:
revised:
29
08
2021
received:
22
12
2020
accepted:
01
10
2021
pubmed:
26
2
2022
medline:
15
6
2022
entrez:
25
2
2022
Statut:
ppublish
Résumé
We propose to use Bayesian optimization (BO) to improve the efficiency of the design selection process in clinical trials. BO is a method to optimize expensive black-box functions, by using a regression as a surrogate to guide the search. In clinical trials, planning test procedures and sample sizes is a crucial task. A common goal is to maximize the test power, given a set of treatments, corresponding effect sizes, and a total number of samples. From a wide range of possible designs, we aim to select the best one in a short time to allow quick decisions. The standard approach to simulate the power for each single design can become too time consuming. When the number of possible designs becomes very large, either large computational resources are required or an exhaustive exploration of all possible designs takes too long. Here, we propose to use BO to quickly find a clinical trial design with high power from a large number of candidate designs. We demonstrate the effectiveness of our approach by optimizing the power of adaptive seamless designs for different sets of treatment effect sizes. Comparing BO with an exhaustive evaluation of all candidate designs shows that BO finds competitive designs in a fraction of the time.
Identifiants
pubmed: 35212423
doi: 10.1002/bimj.202000389
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
948-963Informations de copyright
© 2022 The Authors. Biometrical Journal published by Wiley-VCH GmbH.
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