Adaptive Biased Urn Randomization in Small Strata When Blinding Is Impossible
Abstract
Adaptive biased urn randomization, applied in, e.g., a clinical trial, has certain attractive properties. If stratified randomization is desired, a good balance between group sizes can be guaranteed, even in (very) small strata. Yet treatment assignment may be kept unpredictable, which is necessary to avoid selection bias if blinding is impossible. In the present paper a more flexible urn model is described. The investigator may choose assignment probabilities that strongly depend on the degree of imbalance when the groups are still small, but with a tendency toward complete randomization when the groups become large. It is also possible to keep the difference in group size below a chosen maximum, which is useful if population characteristics may change during the course of a trial. The new urn model includes random permutations and complete randomization as special cases. An extension of the model allows the promotion of unequal group sizes. Some attention is paid to a randomized version of the minimization method.
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Participants were randomized in a 1:1 ratio to experimental or control groups balancing age, sex, years of formal education, cognitive status, and MoCA score using a minimization algorithm.
“Participants were randomized in a 1:1 ratio to the experimental or control groups balancing age, sex, years of formal education, and cognitive status (MCI/NC) and MoCA score. A minimization algorithm was used for randomization (Schouten, 1995).”
Participants will be randomised using a flexible biased urn method that adapts to imbalance between groups over the trial.
“Participants will be randomised to one of the three arms using a flexible biased urn method of randomisation, which adapts to the degree of imbalance between groups in a dynamic manner over the trial [41].”
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- Authors
- Schouten, H. J. A.
- Affiliations
- Maastricht University (NL)
- Corresponding author
- Schouten, Hubert J. A.
- Venue
- Biometrics
- Publisher
- JSTOR
- Published
- 1995-12
- Volume
- 51
- Issue
- 4
- Pages
- 1529
- ISSN
- 0006-341X (ISSN-L)
- OpenAlex
- W2320535868
- Cited by
- 62
- References
- 10
- Citations by year
- 2024: 1 · 2023: 2 · 2021: 6 · 2020: 4 · 2019: 2 · 2018: 1 · 2016: 5 · 2015: 5 · 2014: 1 · 2013: 1 · 2012: 5
- FWCI
- 0.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biometrics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Forskningsrådet om Hälsa, Arbetsliv och Välfärd
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- stratified randomization, unblinding, selection bias, urn models, random permutations, unequal group sizes, minimization method, clinical trials
- MeSH terms
- Biometry, Humans, Random Allocation, Models, Statistical, Bias, Randomized Controlled Trials as Topic, Sample Size
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