Evidence map›Paper›PMID 29490655›Full record

ArticleBMC medicine2018

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

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 and 6 more

2 registry-linked trialsAbstract read
In one paragraph

Article in BMC medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 373 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
373citing papers in PubMed, 7 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

NCT05084898 nacompletednot on this mapstarted 2021, after this paper: background citation

Do Far-UVC Light Devices Reduce the Incidence of Respiratory Viral Illness in Long-term Care Facilities?

TypeinterventionalSponsorKenneth RockwoodRan2021 to 2025Enrolled550ConditionsCOVID-19 Respiratory Infection, Respiratory Virus Infection, Influenza -Like IllnessArmsFar-UVC light as an additional method of disinfection, Placebo fluorescent light
NCT07491419 narecruitingnot on this mapstarted 2026, after this paper: background citation

The Impact of Fast Antimicrobial Sensitivity Testing Tools on Stewardship Antibiotic and Clinical Outcome: a Randomized Clinical Trial Within an Adaptive Platform Trial for Patients With Bloodstream Infections

TypeinterventionalSponsorIstituto Clinico HumanitasRan2026 to 2028Enrolled400ConditionsBloodstream Infection, Gram-Negative Infections, Gram-Positive Infections, Bacteremia SepsisArmsDiagnostic Test: Rapid Antimicrobial Susceptibility Testing (R-AST) guided Stewardship, Standard of Care (SOC)
3 · Its place in the literature

Who cites it

373 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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313 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Philip PallmannDepartment of Mathematics & Statistics, Lancaster University, Lancaster, LA1 4YF, UK. p.pallmann@lancaster.ac.uk.ORCID 0000-0001-8274-9696
Alun W BeddingRoche Products Ltd, Welwyn Garden City, UK.
Babak Choodari-OskooeiMRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK.
Munyaradzi DimairoClinical Trials Research Unit, University of Sheffield, Sheffield, UK.
Laura FlightMedical Statistics Group, University of Sheffield, Sheffield, UK.
Lisa V HampsonDepartment of Mathematics & Statistics, Lancaster University, Lancaster, LA1 4YF, UK.
Jane HolmesCentre for Statistics in Medicine, University of Oxford, Oxford, UK.
Adrian P ManderMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Lang'o OdondiCentre for Statistics in Medicine, University of Oxford, Oxford, UK.
Matthew R SydesMRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK.
Sofía S VillarMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
James M S WasonMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Christopher J WeirUsher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.
Graham M WheelerMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Christina YapCancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham, UK.
Thomas JakiDepartment of Mathematics & Statistics, Lancaster University, Lancaster, LA1 4YF, UK.

Funding

Cancer Research UK 15958Cancer Research UK C22436/A15958Department of Health DRF-2012-05-182Department of Health DRF-2015-08-013Department of Health SRF-2015-08-001Medical Research Council G0800860Medical Research Council MC_EX_UU_G0800814Medical Research Council MC_UP_1302/4Medical Research Council MC_UU_00002/3Medical Research Council MC_UU_00002/6Medical Research Council MC_UU_12023/24Medical Research Council MC_UU_12023/25Medical Research Council MC_UU_12023/29Medical Research Council MR/K025635/1Medical Research Council MR/L004933/1Medical Research Council MR/L004933/1-R/N/P/B1Medical Research Council MR/L004933/2Medical Research Council MR/N028171/1
6 · The paper itself

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.

Indexed as

Clinical Trials as TopicHumansReproducibility of ResultsResearch DesignAdaptive designDesign modificationFlexible designInterim analysisSeamless designStatistical methods

Identifiers

PMID29490655
PMCPMC5830330

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.