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Adaptive designs in clinical trials: why use them, and how to run and report them

Overview of attention for article published in BMC Medicine, February 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)

Mentioned by

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86 tweeters

Citations

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30 Dimensions

Readers on

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129 Mendeley
Title
Adaptive designs in clinical trials: why use them, and how to run and report them
Published in
BMC Medicine, February 2018
DOI 10.1186/s12916-018-1017-7
Pubmed ID
Authors

Philip Pallmann, Alun W. Bedding, Babak Choodari-Oskooei, Munyaradzi Dimairo, Laura Flight, Lisa V. Hampson, Jane Holmes, Adrian P. Mander, Lang’o Odondi, Matthew R. Sydes, Sofía S. Villar, James M. S. Wason, Christopher J. Weir, Graham M. Wheeler, Christina Yap, Thomas Jaki

Abstract

Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial's course in accordance with pre-specified rules. Trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented.We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice.

Twitter Demographics

The data shown below were collected from the profiles of 86 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 129 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 129 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 36 28%
Student > Ph. D. Student 23 18%
Unspecified 17 13%
Student > Master 15 12%
Other 9 7%
Other 28 22%
Unknown 1 <1%
Readers by discipline Count As %
Medicine and Dentistry 41 32%
Unspecified 36 28%
Mathematics 14 11%
Pharmacology, Toxicology and Pharmaceutical Science 8 6%
Biochemistry, Genetics and Molecular Biology 6 5%
Other 23 18%
Unknown 1 <1%

Attention Score in Context

This research output has an Altmetric Attention Score of 51. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 June 2019.
All research outputs
#337,390
of 13,532,334 outputs
Outputs from BMC Medicine
#284
of 2,144 outputs
Outputs of similar age
#14,136
of 270,559 outputs
Outputs of similar age from BMC Medicine
#1
of 1 outputs
Altmetric has tracked 13,532,334 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,144 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 35.0. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 270,559 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them