Literature DB >> 34520850

A review found small variable blocking schemes may not protect against selection bias in randomized controlled trials.

Laura Clark1, Lauren Burke2, Rachel Margaret Carr2, Elizabeth Coleman2, Gareth Roberts2, David J Torgerson2.   

Abstract

OBJECTIVE: Blocking is associated with prediction of the allocation sequence and subversion. This paper explores if blocking strategies lead to an increase in baseline age heterogeneity (a marker for potential subversion) and, whether the use of blocking is changing over time. STUDY DESIGN AND SETTINGS: The British Medical Journal, Journal of the American Medical Association, The Lancet and the New England Journal of Medicine were hand searched to identify open RCTs published in January between 2001 and 2020. To explore heterogeneity of baseline age meta-analyses were performed on trials implementing blocking, minimization, and simple randomization.
RESULTS: One hundred seventy-nine open RCTs were identified: nine (5.0%) undertook simple randomization, 104 (58.1%) blocking, 25 (13.9%) minimization, and one (0.6%) both. Baseline age heterogeneity of 24% (P= 0.02) was observed in all trials implementing blocking, 62% (P = 0.001) in trials implementing a fixed block of four, 40% (P = 0.07) implementing variable blocks including a 2 and 0% for both simple randomization and minimization. Small block sizes are implemented in modern trials.
CONCLUSION: Variable block sizes including two are associated with subversion and should not be implemented. If center only stratification is necessary, it should be used alongside larger blocking schemes. Authors should consider alternative methods to restrict randomization.
Copyright © 2021. Published by Elsevier Inc.

Entities:  

Keywords:  Allocation concealment; Bias; Methodology; Randomization; Randomized controlled trials; Research design

Mesh:

Year:  2021        PMID: 34520850     DOI: 10.1016/j.jclinepi.2021.09.009

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  1 in total

1.  Risk of selection bias assessment in the NINDS rt-PA stroke study.

Authors:  Ravi Garg; Steffen Mickenautsch
Journal:  BMC Med Res Methodol       Date:  2022-06-15       Impact factor: 4.612

  1 in total

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