Mathematical programming tools for randomization purposes in small two‐arm clinical trials: A case study with real data

Modern randomization methods in clinical trials are invariably adaptive, meaning that the assignment of the next subject to a treatment group uses the accumulated information in the trial. Some of the recent adaptive randomization methods use mathematical programming to construct attractive clinical...

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Published in:Pharmaceutical statistics : the journal of the pharmaceutical industry Vol. 23; no. 6; pp. 794 - 812
Main Authors: Vazquez, Alan R., Wong, Weng‐Kee
Format: Journal Article
Language:English
Published: Chichester, UK John Wiley & Sons, Inc 01.11.2024
Wiley Subscription Services, Inc
Subjects:
Adaptive Clinical Trials as Topic
Clinical trials
covariate‐adaptive trial
energy distance
Humans
Main Paper
Mathematical programming
minimization method
Models, Statistical
prior information
Random Allocation
Randomized Controlled Trials as Topic - methods
Randomized Controlled Trials as Topic - statistics & numerical data
Research Design - statistics & numerical data
Sample Size
minimization method
energy distance
prior information
covariate‐adaptive trial
ISSN:1539-1604, 1539-1612, 1539-1612
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Abstract Modern randomization methods in clinical trials are invariably adaptive, meaning that the assignment of the next subject to a treatment group uses the accumulated information in the trial. Some of the recent adaptive randomization methods use mathematical programming to construct attractive clinical trials that balance the group features, such as their sizes and covariate distributions of their subjects. We review some of these methods and compare their performance with common covariate‐adaptive randomization methods for small clinical trials. We introduce an energy distance measure that compares the discrepancy between the two groups using the joint distribution of the subjects' covariates. This metric is more appealing than evaluating the discrepancy between the groups using their marginal covariate distributions. Using numerical experiments, we demonstrate the advantages of the mathematical programming methods under the new measure. In the supplementary material, we provide R codes to reproduce our study results and facilitate comparisons of different randomization procedures.
AbstractList Modern randomization methods in clinical trials are invariably adaptive, meaning that the assignment of the next subject to a treatment group uses the accumulated information in the trial. Some of the recent adaptive randomization methods use mathematical programming to construct attractive clinical trials that balance the group features, such as their sizes and covariate distributions of their subjects. We review some of these methods and compare their performance with common covariate‐adaptive randomization methods for small clinical trials. We introduce an energy distance measure that compares the discrepancy between the two groups using the joint distribution of the subjects' covariates. This metric is more appealing than evaluating the discrepancy between the groups using their marginal covariate distributions. Using numerical experiments, we demonstrate the advantages of the mathematical programming methods under the new measure. In the supplementary material, we provide R codes to reproduce our study results and facilitate comparisons of different randomization procedures.
Modern randomization methods in clinical trials are invariably adaptive, meaning that the assignment of the next subject to a treatment group uses the accumulated information in the trial. Some of the recent adaptive randomization methods use mathematical programming to construct attractive clinical trials that balance the group features, such as their sizes and covariate distributions of their subjects. We review some of these methods and compare their performance with common covariate-adaptive randomization methods for small clinical trials. We introduce an energy distance measure that compares the discrepancy between the two groups using the joint distribution of the subjects' covariates. This metric is more appealing than evaluating the discrepancy between the groups using their marginal covariate distributions. Using numerical experiments, we demonstrate the advantages of the mathematical programming methods under the new measure. In the supplementary material, we provide R codes to reproduce our study results and facilitate comparisons of different randomization procedures.Modern randomization methods in clinical trials are invariably adaptive, meaning that the assignment of the next subject to a treatment group uses the accumulated information in the trial. Some of the recent adaptive randomization methods use mathematical programming to construct attractive clinical trials that balance the group features, such as their sizes and covariate distributions of their subjects. We review some of these methods and compare their performance with common covariate-adaptive randomization methods for small clinical trials. We introduce an energy distance measure that compares the discrepancy between the two groups using the joint distribution of the subjects' covariates. This metric is more appealing than evaluating the discrepancy between the groups using their marginal covariate distributions. Using numerical experiments, we demonstrate the advantages of the mathematical programming methods under the new measure. In the supplementary material, we provide R codes to reproduce our study results and facilitate comparisons of different randomization procedures.
Author Vazquez, Alan R.
Wong, Weng‐Kee
AuthorAffiliation 1 School of Engineering and Sciences Tecnologico de Monterrey Monterrey Nuevo Leon Mexico
2 Department of Biostatistics University of California Los Angeles California USA
AuthorAffiliation_xml – name: 2 Department of Biostatistics University of California Los Angeles California USA
– name: 1 School of Engineering and Sciences Tecnologico de Monterrey Monterrey Nuevo Leon Mexico
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  givenname: Alan R.
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  email: wkwong@ucla.edu
  organization: University of California
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Keywords minimization method
energy distance
prior information
covariate‐adaptive trial
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Snippet Modern randomization methods in clinical trials are invariably adaptive, meaning that the assignment of the next subject to a treatment group uses the...
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StartPage 794
SubjectTerms Adaptive Clinical Trials as Topic
Clinical trials
covariate‐adaptive trial
energy distance
Humans
Main Paper
Mathematical programming
minimization method
Models, Statistical
prior information
Random Allocation
Randomized Controlled Trials as Topic - methods
Randomized Controlled Trials as Topic - statistics & numerical data
Research Design - statistics & numerical data
Sample Size
Title Mathematical programming tools for randomization purposes in small two‐arm clinical trials: A case study with real data
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fpst.2388
https://www.ncbi.nlm.nih.gov/pubmed/38613324
https://www.proquest.com/docview/3133567990
https://www.proquest.com/docview/3038435484
https://pubmed.ncbi.nlm.nih.gov/PMC11602943
Volume 23
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