Evidence mapPaperPMID 34399696Full record

Trial reportBMC medical research methodology2021

A roadmap to using randomization in clinical trials.

Vance W Berger, Louis Joseph Bour, Kerstine Carter, Jonathan J Chipman, Colin C Everett, Nicole Heussen, Catherine Hewitt, Ralf-Dieter Hilgers, Yuqun Abigail Luo, Jone Renteria and 4 more

2 registry-linked trialsAbstract readRandomized Controlled Trial
In one paragraph

Trial report in BMC medical research methodology, 2021. 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 59 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
59citing papers in PubMed, 1 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.

NCT05196269 naactive not recruitingnot on this mapstarted 2023, after this paper: background citation

Comparing Decision on Match of Expectations and Aesthetics Using a Conventional Versus a Cloud-based Healthcare Platform Approach in Breast Cancer Patients Proposed for Locoregional Treatment: A Prospective Randomized Trial

TypeinterventionalSponsorFundacao ChampalimaudRan2023 to 2026Enrolled1,030ConditionsBreast CancerArmsArtificial Intelligence and Digital Health Arm
NCT06452134 phase4unknown statusnot on this mapstarted 2024, after this paper: background citation

Evaluation the Effect of Coenzyme Q10 on Tissue Healing Process in Patients Undergoing Wisdom Tooth Extraction in the City of Sanandaj-Iran in 1402: a Double-blinded Clinical Trial Study

TypeinterventionalSponsorZahra NejatiRan2024 to 2025Enrolled70ConditionsMolar, Third, Temporomandibular Joint Disorders, Dry Socket, Coenzyme Q10ArmsCoenzyme Q10 100 MG Oral Tablet, Placebo
3 · Its place in the literature

Who cites it

59 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

14 authors.

Vance W BergerNational Institutes of Health, Bethesda, MD, USA.
Louis Joseph BourBoehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany.
Kerstine CarterBoehringer-Ingelheim Pharmaceuticals Inc, Ridgefield, CT, USA.
Jonathan J ChipmanPopulation Health Sciences, University of Utah School of Medicine, Salt Lake City UT, USA.ORCID 0000-0002-3021-2376
Colin C EverettClinical Trials Research Unit, University of Leeds, Leeds, UK.ORCID 0000-0002-9788-840X
Nicole HeussenRWTH Aachen University, Aachen, Germany.ORCID 0000-0002-6134-7206
Catherine HewittYork Trials Unit, Department of Health Sciences, University of York, York, UK.ORCID 0000-0002-0415-3536
Ralf-Dieter HilgersRWTH Aachen University, Aachen, Germany.ORCID 0000-0002-5945-1119
Yuqun Abigail LuoFood and Drug Administration, Silver Spring, MD, USA.
Jone RenteriaOpen University of Catalonia (UOC) and the University of Barcelona (UB), Barcelona, Spain.
Yevgen RyeznikBioPharma Early Biometrics & Statistical Innovations, Data Science & AI, R&D BioPharmaceuticals, AstraZeneca, Gothenburg, Sweden.ORCID 0000-0003-2997-8566
Oleksandr SverdlovEarly Development Analytics, Novartis Pharmaceuticals Corporation, NJ, East Hanover, USA. alex.sverdlov@novartis.com.ORCID 0000-0002-1626-2588
Diane UschnerBiostatistics Center & Department of Biostatistics and Bioinformatics, George Washington University, DC, Washington, USA.ORCID 0000-0002-7858-796X
Randomization Innovative Design Scientific Working Group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRandomization is the foundation of any clinical trial involving treatment comparison. It helps mitigate selection bias, promotes similarity of treatment groups with respect to important known and unknown confounders, and contributes to the validity of statistical tests. Various restricted randomization procedures with different probabilistic structures and different statistical properties are available. The goal of this paper is to present a systematic roadmap for the choice and application of a restricted randomization procedure in a clinical trial.

methodsWe survey available restricted randomization procedures for sequential allocation of subjects in a randomized, comparative, parallel group clinical trial with equal (1:1) allocation. We explore statistical properties of these procedures, including balance/randomness tradeoff, type I error rate and power. We perform head-to-head comparisons of different procedures through simulation under various experimental scenarios, including cases when common model assumptions are violated. We also provide some real-life clinical trial examples to illustrate the thinking process for selecting a randomization procedure for implementation in practice.

resultsRestricted randomization procedures targeting 1:1 allocation vary in the degree of balance/randomness they induce, and more importantly, they vary in terms of validity and efficiency of statistical inference when common model assumptions are violated (e.g. when outcomes are affected by a linear time trend; measurement error distribution is misspecified; or selection bias is introduced in the experiment). Some procedures are more robust than others. Covariate-adjusted analysis may be essential to ensure validity of the results. Special considerations are required when selecting a randomization procedure for a clinical trial with very small sample size.

conclusionsThe choice of randomization design, data analytic technique (parametric or nonparametric), and analysis strategy (randomization-based or population model-based) are all very important considerations. Randomization-based tests are robust and valid alternatives to likelihood-based tests and should be considered more frequently by clinical investigators.

Indexed as

Random AllocationComputer SimulationHumansLikelihood FunctionsSample SizeSelection BiasBalanceRandomization-based testRestricted randomization designValidity

Identifiers

PMID34399696
PMCPMC8366748

What Socratic 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 Socratic graph.