Evidence map›Paper›PMID 38418968›Full record

Trial reportBMC medical research methodology2024

Selecting a randomization method for a multi-center clinical trial with stochastic recruitment considerations.

Oleksandr Sverdlov, Yevgen Ryeznik, Volodymyr Anisimov, Olga M Kuznetsova, Ruth Knight, Kerstine Carter, Sonja Drescher, Wenle Zhao

Abstract readRandomized Controlled TrialMulticenter Study
In one paragraph

Trial report in BMC medical research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

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

  1. Pooled it
  2. Trial
  3. Article
  4. Review
  5. Article
  6. Review
  7. Forced randomization: the what, why, and how.BMC medical research methodology · 2024
    Article
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

8 authors.

Oleksandr Sverdlov *Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA. alex.sverdlov@novartis.com.ORCID 0000-0002-1626-2588
Yevgen Ryeznik *Department of Pharmacy, Uppsala University, Uppsala, Sweden.ORCID 0000-0003-2997-8566
Volodymyr AnisimovAmgen Ltd., London, UK.ORCID 0000-0003-1230-1794
Olga M KuznetsovaMerck & Co., Inc., Rahway, NJ, USA.ORCID 0000-0002-7037-3641
Ruth KnightLiverpool Clinical Trials Centre, University of Liverpool, Merseyside, Liverpool, UK.ORCID 0000-0001-6810-2845
Kerstine CarterBoehringer-Ingelheim Pharmaceuticals Inc, Ridgefield, CT, USA.ORCID 0009-0004-6144-2344
Sonja DrescherBoehringer-Ingelheim Pharma GmbH & Co. KG, Biberach, Germany.
Wenle ZhaoMedical University of South Carolina, Charleston, SC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe design of a multi-center randomized controlled trial (RCT) involves multiple considerations, such as the choice of the sample size, the number of centers and their geographic location, the strategy for recruitment of study participants, amongst others. There are plenty of methods to sequentially randomize patients in a multi-center RCT, with or without considering stratification factors. The goal of this paper is to perform a systematic assessment of such randomization methods for a multi-center 1:1 RCT assuming a competitive policy for the patient recruitment process.

methodsWe considered a Poisson-gamma model for the patient recruitment process with a uniform distribution of center activation times. We investigated 16 randomization methods (4 unstratified, 4 region-stratified, 4 center-stratified, 3 dynamic balancing randomization (DBR), and a complete randomization design) to sequentially randomize

resultsMaximum tolerated imbalance (MTI) randomization methods such as big stick design, Ehrenfest urn design, and block urn design result in a better balance-randomness tradeoff than the conventional permuted block design (PBD) with or without stratification. Unstratified randomization, region-stratified randomization, and center-stratified randomization provide control of imbalance at a chosen level (trial, region, or center) but may fail to achieve balance at the other two levels. By contrast, DBR does a very good job controlling imbalance at all 3 levels while maintaining the randomized nature of treatment allocation. Adding more centers into the study helps accelerate the recruitment process but at the expense of increasing the number of centers that recruit very few (or no) patients-which may increase center-level imbalances for center-stratified and DBR procedures. Increasing the block size or the MTI threshold(s) may help obtain designs with improved randomness-balance tradeoff.

conclusionsThe choice of a randomization method is an important component of planning a multi-center RCT. Dynamic balancing randomization with carefully chosen MTI thresholds could be a very good strategy for trials with the competitive policy for patient recruitment.

Indexed as

Research DesignHumansPatient SelectionRandom AllocationSample SizeAllocation randomnessMaximum tolerated imbalanceMulti-center clinical trialPoisson-gamma modelRecruitment time

Identifiers

PMID38418968
PMCPMC10900599

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Registered trials

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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.