Please use this identifier to cite or link to this item: https://doi.org/10.5705/ss.202020.0412
Title: A STATISTICAL APPROACH TO ADAPTIVE PARAMETER TUNING IN NATURE-INSPIRED OPTIMIZATION AND OPTIMAL SEQUENTIAL DESIGN OF DOSE-FINDING TRIALS
Authors: Choi, Kwok Pui 
Lai, Tze Leung
TONG XIN 
Wong, Weng Kee
Keywords: Adaptive group sequential designs
compound optimality criterion for toxicity and efficacy
locally D-optimal and c-optimal designs
Issue Date: 1-Oct-2021
Publisher: Academia Sinica
Citation: Choi, Kwok Pui, Lai, Tze Leung, TONG XIN, Wong, Weng Kee (2021-10-01). A STATISTICAL APPROACH TO ADAPTIVE PARAMETER TUNING IN NATURE-INSPIRED OPTIMIZATION AND OPTIMAL SEQUENTIAL DESIGN OF DOSE-FINDING TRIALS. STATISTICA SINICA 31 : 1-21. ScholarBank@NUS Repository. https://doi.org/10.5705/ss.202020.0412
Abstract: Nature-inspired metaheuristic algorithms have become increasingly popular in the last couple of decades, and now constitute a major toolbox for tackling complex high-dimensional optimization problems. Using group sequential experimentation, adaptive design, multi-armed bandits, and bootstrap resampling methods, this study develops a novel statistical methodology for efficient and systematic group sequential selection of the tuning parameters, which are widely recognized as pivotal to the success of metaheuristic optimization algorithms in practice, as new information accumulates during the course of an experiment. The methodology is applied to compute optimal experimental designs in nonlinear regression models, and is illustrated with solutions of long-standing optimal design problems in early-phase dose-finding oncology trials.
Source Title: STATISTICA SINICA
URI: https://scholarbank.nus.edu.sg/handle/10635/211396
ISSN: 1017-0405
DOI: 10.5705/ss.202020.0412
Appears in Collections:Staff Publications
Elements

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
SS-2020-0412_fp.pdf145.04 kBAdobe PDF

OPEN

PublishedView/Download

Page view(s)

133
checked on Feb 2, 2023

Download(s)

15
checked on Feb 2, 2023

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.