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Lookup NU author(s): Dr Faye Williamson
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2021 The Author(s). The design of sequential experiments and, in particular, randomised controlled trials involves a trade-off between operational characteristics such as statistical power, estimation bias and patient benefit. The family of randomisation procedures referred to as Constrained Randomised Dynamic Programming (CRDP), which is set in the Bayesian decision-theoretic framework, can be used to balance these competing objectives. A generalisation and novel interpretation of CRDP is proposed to highlight its inherent flexibility to adapt to a variety of practicalities and align with individual trial objectives. CRDP, as with most response-adaptive randomisation procedures, hinges on the limiting assumption of patient responses being available before allocation of the next patient. This forms one of the greatest barriers to their implementation in practice which, despite being an important research question, has not received a thorough treatment. Therefore, motivated by the existing gap between the theory of response-adaptive randomisation (which is abundant with proposed methods in the immediate response setting) and clinical practice (in which responses are typically delayed), the performance of CRDP in the presence of fixed and random delays is evaluated. Simulation results show that CRDP continues to offer patient benefit gains over alternative procedures and is relatively robust to delayed responses. To compensate for a fixed delay, a method which adjusts the time horizon used in the optimisation objective is proposed and its performance illustrated.
Author(s): Williamson SF, Jacko P, Jaki T
Publication type: Article
Publication status: Published
Journal: Computational Statistics and Data Analysis
Year: 2022
Volume: 174
Print publication date: 01/10/2022
Online publication date: 07/12/2021
Acceptance date: 01/12/2021
Date deposited: 29/07/2022
ISSN (print): 0167-9473
ISSN (electronic): 1872-7352
Publisher: Elsevier BV
URL: https://doi.org/10.1016/j.csda.2021.107407
DOI: 10.1016/j.csda.2021.107407
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