Strategies for informed sample size reduction in adaptive controlled clinical trials
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Clinical trial adaptation refers to any adjustment of the trial protocol after the onset of the trial. The main goal is to make the process of introducing new medical interventions to patients more efficient. The principal challenge, which is an outstanding research problem, is to be found in the question of how adaptation should be performed so as to minimize the chance of distorting the outcome of the trial. In this paper, we propose a novel method for achieving this. Unlike most of the previously published work, our approach focuses on trial adaptation by sample size adjustment, i.e. by reducing the number of trial participants in a statistically informed manner. Our key idea is to select the sample subset for removal in a manner which minimizes the associated loss of information. We formalize this notion and describe three algorithms which approach the problem in different ways, respectively, using (i) repeated random draws, (ii) a genetic algorithm, and (iii) what we term pair-wise sample compatibilities. Experiments on simulated data demonstrate the effectiveness of all three approaches, with a consistently superior performance exhibited by the pair-wise sample compatibilities-based method.
Arandelovic , O 2017 , ' Strategies for informed sample size reduction in adaptive controlled clinical trials ' EURASIP Journal on Advances in Signal Processing . https://doi.org/10.1186/s13634-017-0510-z
EURASIP Journal on Advances in Signal Processing
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
DescriptionSpecial issue on Biomedical Informatics with Optimization and Machine Learning
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