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Improving adaptive seamless designs through Bayesian optimization
Author(s) -
Richter Jakob,
Friede Tim,
Rahnenführer Jörg
Publication year - 2022
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.202000389
Subject(s) - computer science , sample size determination , bayesian optimization , bayesian probability , selection (genetic algorithm) , design of experiments , set (abstract data type) , range (aeronautics) , process (computing) , reliability engineering , data mining , mathematical optimization , machine learning , artificial intelligence , statistics , mathematics , engineering , programming language , aerospace engineering , operating system
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.