Minimization, by its nature, precludes allocation concealment, and invites selection bias
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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”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [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)
- PMC
- PMC2930067
- 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
- green
- www.ncbi.nlm.nih.gov
- 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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