Cited by 40Statistical Methods in Clinical TrialsAdvanced Causal Inference TechniquesOptimal Experimental Design Methods

Minimization, by its nature, precludes allocation concealment, and invites selection bias

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Berger, Vance W.·2010·Contemporary Clinical Trials

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Crossref · 10.1016/j.cct.2010.05.001

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  • If some type of restricted randomisation approach is to be used, in particular blocked randomisation or minimisation, then knowledge of the specific details could lead to bias.

    “If some type of restricted randomisation approach is to be used, in particular blocked randomisation or minimisation, then knowledge of the specific details could lead to bias.342 343”

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Authors
Berger, V. W.
Affiliations
National Cancer Institute (US)
Corresponding author
Berger, Vance W.
Venue
Contemporary Clinical Trials
Publisher
Elsevier BV
Published
2010-09
Volume
31
Issue
5
Pages
406
ISSN
1551-7144 (ISSN-L)
OpenAlex
W2065130036
Cited by
40
References
5
Citations by year
2026: 1 · 2025: 1 · 2023: 2 · 2021: 4 · 2020: 2 · 2019: 2 · 2018: 3 · 2017: 1 · 2015: 6 · 2014: 5 · 2013: 4 · 2012: 5
Type
journal article
Language
en
Open access
Locations
  • Contemporary Clinical Trials (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • DigitalGeorgetown (Georgetown University Library) (submittedVersion)
Funders
National Institutes of Health (Z99 CA999999)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
Keywords
minimization, allocation concealment, selection bias, randomization methods, covariate balance, block randomization, stratified randomization, prognostic factors, clinical trial design
MeSH terms
Algorithms, Clinical Trials as Topic, Data Collection, Data Interpretation, Statistical, Humans, Random Allocation, Research Design, Selection Bias, Patient Selection, Research Subjects

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