ArticlePloS one2015
Clinical Trial Adaptation by Matching Evidence in Complementary Patient Sub-groups of Auxiliary Blinding Questionnaire Responses.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Blinding, sham, and treatment effects in randomized controlled trials for back pain in 2000-2019: A review and meta-analytic approach.Clinical trials (London, England) · 2021Pooled it
- Complex temporal topic evolution modelling using the Kullback-Leibler divergence and the Bhattacharyya distance.EURASIP journal on bioinformatics & systems biology · 2016Article
- Random Guess and Wishful Thinking are the Best Blinding Scenarios.Contemporary clinical trials communications · 2016Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical trial adaptation refers to any adjustment of the trial protocol after the onset of the trial. Such adjustment may take on various forms, including the change in the dose of administered medicines, the frequency of administering an intervention, the number of trial participants, or the duration of the trial, to name just some possibilities. The main goal is to make the process of introducing new medical interventions to patients more efficient, either by reducing the cost or the time associated with evaluating their safety and efficacy. 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. We adopt a stratification framework recently proposed for the analysis of trial outcomes in the presence of imperfect blinding and based on the administration of a generic auxiliary questionnaire that allows the participants to express their belief concerning the assigned intervention (treatment or control). We show that this data, together with the primary measured variables, can be used to make the probabilistically optimal choice of the particular sub-group a participant should be removed from if trial size reduction is desired. Extensive experiments on a series of simulated trials are used to illustrate the effectiveness of our method.
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Registered trials
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