The use of minimization in clinical trials
Abstract
Since its introduction in 1974 the use of the term Minimization has been broadened to include other algorithms. All algorithms use patient characteristics to determine the assignment that produces the best overall balance between treatment groups. They differ in whether or not they use all of the data from each previously assigned subject to assign subsequent subjects so the methods are classified as complete or partial minimization. PubMed, Citation Index and Cochrane searches determined the frequency of articles using these types of minimization and a subset was selected for detailed review regarding the adequacy of the usage and reporting of minimization. In the past 10 years usage has increased three fold over the previous decade but is still less than 2% of clinical trials. None of the studies makes maximum use of minimization and they are not following good reporting practices. Concerns about the use of minimization have involved selection bias and statistical analysis. Several modifications to minimization are suggested to reduce the possibility of selection bias so that adding randomization will rarely be required. Separating primary and secondary analyses can avoid the statistical problems that minimization poses. The two types of analyses are distinguished by opposite limiting signs, providing reliable, simplified statistical results. This will improve data utilization and make clinical trials more reproducible. Minimization should be the method of choice in assigning subjects in all clinical trials.
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A minimisation approach to randomisation will be taken in order to balance participant characteristics and numbers across conditions.
“Given that entire schools will be randomised, a minimisation approach to randomisation will be taken in order to balance participant characteristics and numbers across conditions [22, 23].”
From Silence is deadly: a cluster-randomised controlled trial of a mental health help-seeking intervention for young men (2017) · cites this paper as [23]
Cites
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“They are based on optimum design theory [5] and have important differences from the algorithms described by Pocock and Simon.”
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The Sources of Strength Australia Project: study protocol for a cluster randomised controlled trial
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- Authors
- Taves, D. R.
- Affiliations
- University of Washington (US)
- Corresponding author
- Taves, Donald R.
- ORCID
- Taves, Donald R.
- Venue
- Contemporary Clinical Trials
- Publisher
- Elsevier BV
- Published
- 2010-03
- Volume
- 31
- Issue
- 2
- Pages
- 180-184
- ISSN
- 1551-7144 (ISSN-L)
- OpenAlex
- W2027940431
- Cited by
- 140
- References
- 18
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 13 · 2023: 13 · 2022: 6 · 2021: 11 · 2020: 11 · 2019: 9 · 2018: 3 · 2017: 8 · 2016: 8 · 2015: 5 · 2014: 10 · 2013: 7 · 2012: 13
- FWCI
- 7.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Contemporary Clinical Trials (publishedVersion)
- PubMed (publishedVersion)
- 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, treatment allocation, randomization, selection bias, reporting practices, data utilization, reproducibility, clinical trials
- MeSH terms
- Algorithms, Clinical Trials as Topic, Data Collection, Data Interpretation, Statistical, Humans, Random Allocation, Research Design, Reproducibility of Results, Models, Statistical, Selection Bias, Patient Selection
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