Minimization–reducing predictability for multi-centre trials whilst retaining balance within centre

Brown, Sarah; Thorpe, Helen; Hawkins, Kim; Brown, Julia

doi:10.1002/sim.2391

Cited by 97Statistical Methods in Clinical TrialsAdvanced Causal Inference TechniquesOptimal Experimental Design Methods

Minimization–reducing predictability for multi-centre trials whilst retaining balance within centre

Brown, Sarah, Thorpe, Helen, Hawkins, Kim, Brown, Julia·2005·Statistics in Medicine

DOI 10.1002/sim.2391

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Abstract

Minimization is often used to assign patients to treatment groups to ensure good balance in patient numbers within centre and other prognostic factors. Balance within centre is preferable since large imbalances between treatment arms may have logistical implications for centres, such as cost and resource implications. However, recent concern over high predictability of treatment allocation by centres when using minimization has caused this method to be questioned. We used data from current clinical trials to assess predictability and summarize subsequent within-centre imbalances with the aim of finding the most effective minimization method for reducing predictability whilst still retaining sufficient balance within centre, when randomization is to one of two treatments. We compared prediction rates and imbalances for deterministic minimization, and minimization incorporating various random elements, p (p=0.95,0.90,0.80,0.75,0.70). We also compared prediction rates and imbalance when centre was and was not included as a stratification factor. Incorporating a random element proved successful in reducing prediction rates whilst minimizing the inevitable increase in within-centre imbalance, whereas excluding centre as a stratification factor incurred major within-centre imbalance. We therefore suggest that minimization can still be used, and that centre can be included as a stratification factor, but a random element has to be incorporated into the minimization algorithm. Minimization incorporating a random element of 0.80 is the most efficient method to use based upon the simulations undertaken in this study of real clinical trial data using different probabilities of allocation.

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Randomization used minimization to ensure balance between groups in potential confounders including insomnia severity, apnea-hypopnea index, gender, site, prior CPAP use, and age.
“Following a diagnosis of insomnia, participants were randomized to either CBT-i or TAU using minimization (MinimPy [34]) to ensure balance between groups in potential confounders including insomnia severity; apnea-hypopnea index (AHI); gender; site; prior CPAP use; and age.”
From Cognitive and behavioral therapy for insomnia increases the use of continuous positive airway pressure therapy in obstructive sleep apnea participants with comorbid insomnia: A randomized clinical trial (2019) · cites this paper as [34]

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Marian, I. R., Goff, M., Williams, J. A. E., Gulati, M., Chester‐Jones, M., Francis, A., Watson, M., Vincent, T. L., Woollacott, S., Mackworth-Young, C. G., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14843244
Additional file 1 of Choosing and evaluating randomisation methods in clinical trials: a qualitative study
Bruce, C. L., Iflaifel, M., Montgomery, A., Ogollah, R. O., Sprange, K., & Partlett, C. (2024) · Figshare · doi:10.6084/m9.figshare.25449049
Additional file 3 of Choosing and evaluating randomisation methods in clinical trials: a qualitative study
Bruce, C. L., Iflaifel, M., Montgomery, A., Ogollah, R. O., Sprange, K., & Partlett, C. (2024) · Figshare · doi:10.6084/m9.figshare.26695710
Additional file 2 of Choosing and evaluating randomisation methods in clinical trials: a qualitative study
Bruce, C. L., Iflaifel, M., Montgomery, A. A., Ogollah, R. O., Sprange, K., & Partlett, C. (2024) · Figshare · doi:10.6084/m9.figshare.25449133
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Minimisation-reducing predictability for multicentre trials
Brown, S., Thorpe, H., Hawkins, K., & Brown, J. M. (2005) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York)
Self-citation
Investigating the relationship between predictability and imbalance in minimisation: a simulation study
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Authors: Brown, S., Thorpe, H., Hawkins, K., & Brown, J.
Affiliations: University of Leeds (GB)
Corresponding author: Brown, Sarah
ORCID: Brown, Sarah
Brown, Julia
Venue: Statistics in Medicine
Publisher: Wiley
Published: 2005-11-30
Volume: 24
Issue: 24
Pages: 3715-3727
ISSN: 0277-6715 (ISSN-L)
1097-0258
DOI: 10.1002/sim.2391
OpenAlex: W2098974730
Cited by: 97
References: 8
Citations by year: 2026: 3 · 2025: 2 · 2024: 10 · 2023: 6 · 2022: 2 · 2021: 9 · 2020: 4 · 2019: 4 · 2018: 7 · 2017: 7 · 2016: 6 · 2015: 8 · 2014: 7 · 2013: 4 · 2012: 5
FWCI: 2.76
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Statistics in Medicine (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, predictability, multi-centre trials, randomization, stratification
MeSH terms: Algorithms, United Kingdom, Humans, Random Allocation, Models, Statistical, Multicenter Studies as Topic, Patient Selection

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Minimization–reducing predictability for multi-centre trials whilst retaining balance within centre

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