Covariates Adaptive Randomization Designs in Clinical Trials: A Comparative Study

Montasir Ahmed Osman

doi:doi:10.11648/j.ijsd.20160204.15

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Covariates Adaptive Randomization Designs in Clinical Trials: A Comparative Study

Montasir Ahmed Osman

Published in International Journal of Statistical Distributions and Applications (Volume 2, Issue 4)

Received: 29 September 2016 Accepted: 24 November 2016 Published: 30 December 2016

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Abstract

This paper investigated in covariate adaptive randomization designs, which are used to reduce covariate variables imbalance between treatments in clinical trials. Critical percentage and imbalance minimization methods are compared each one to another, and both are compared with pure randomization method in term of imbalance. The comparison is intended to show which method has minimum imbalance at three covariate variables with twelve single layers and three sample sizes 10, 20 and 100. The results which carried out from the simulation experiment clearly shown that the performance of critical percentage approach is closely similar to imbalance minimization method in full balance case as well as maximum imbalance at all sample sizes. And pure randomization method has the maximum imbalance compared to others at each sample size.

Published in	International Journal of Statistical Distributions and Applications (Volume 2, Issue 4)
DOI	10.11648/j.ijsd.20160204.15
Page(s)	72-75
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Clinical Trials, Imbalance, Minimization, Randomization

References

[1]	Chow, S. C., Chang, M., and Pong, A. (2005). Statistical consideration of adaptive methods in clinical development. Journal of Biopharmaceutical Statistics, 15, 575–591.
[2]	Liu, Q., Proschan, M. A., and Pledger, G. W. (2002). A unified theory of two stage adaptive designs. Journal of American Statistical Association, 97, 1034–1041.
[3]	Chow, Shein-Chung and Chang, Mark (2007). Adaptive design methods in clinical trials. Taylor & Francis Group, Boca Raton.
[4]	FDA (2010). Guidance for industry. Adaptive design clinical trials for drugs and biologics (draft guidance). The United States Food and Drug Administration, Rockville.
[5]	Pocock, S. J. and Simon R. (1975). “Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trials,” Biometrics 31, 103–115.
[6]	Zelen, M. (1974). “The randomization and stratification of patients to clinical trials,” Journal of Chronic Diseases 28, 365–375.
[7]	Wei, L. J. and Durham, S. (1978). “The randomized play-the-winner rule in medical trials,” Journal of American Statistical Association 73, 840–843.
[8]	Atkinson, A. C. (1982). “Optimum biased coin designs for sequential clinical trials with prognostic factors,” Biometrika 69, 61–67.
[9]	Birkett, N. J. (1985). “Adaptive allocation in randomized controlled trials,” Controlled Clinical Trials 6, 146–155.
[10]	Osman, Montasir Ahmed (2015). “A Contribution to Adaptive Randomization,” Journal of Natural and Medical Sciences 16,124-129.

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APA Style

Montasir Ahmed Osman. (2016). Covariates Adaptive Randomization Designs in Clinical Trials: A Comparative Study. International Journal of Statistical Distributions and Applications, 2(4), 72-75. https://doi.org/10.11648/j.ijsd.20160204.15

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ACS Style

Montasir Ahmed Osman. Covariates Adaptive Randomization Designs in Clinical Trials: A Comparative Study. Int. J. Stat. Distrib. Appl. 2016, 2(4), 72-75. doi: 10.11648/j.ijsd.20160204.15

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AMA Style

Montasir Ahmed Osman. Covariates Adaptive Randomization Designs in Clinical Trials: A Comparative Study. Int J Stat Distrib Appl. 2016;2(4):72-75. doi: 10.11648/j.ijsd.20160204.15

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@article{10.11648/j.ijsd.20160204.15,
  author = {Montasir Ahmed Osman},
  title = {Covariates Adaptive Randomization Designs in Clinical Trials: A Comparative Study},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {2},
  number = {4},
  pages = {72-75},
  doi = {10.11648/j.ijsd.20160204.15},
  url = {https://doi.org/10.11648/j.ijsd.20160204.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20160204.15},
  abstract = {This paper investigated in covariate adaptive randomization designs, which are used to reduce covariate variables imbalance between treatments in clinical trials. Critical percentage and imbalance minimization methods are compared each one to another, and both are compared with pure randomization method in term of imbalance. The comparison is intended to show which method has minimum imbalance at three covariate variables with twelve single layers and three sample sizes 10, 20 and 100. The results which carried out from the simulation experiment clearly shown that the performance of critical percentage approach is closely similar to imbalance minimization method in full balance case as well as maximum imbalance at all sample sizes. And pure randomization method has the maximum imbalance compared to others at each sample size.},
 year = {2016}
}

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TY  - JOUR
T1  - Covariates Adaptive Randomization Designs in Clinical Trials: A Comparative Study
AU  - Montasir Ahmed Osman
Y1  - 2016/12/30
PY  - 2016
N1  - https://doi.org/10.11648/j.ijsd.20160204.15
DO  - 10.11648/j.ijsd.20160204.15
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 72
EP  - 75
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20160204.15
AB  - This paper investigated in covariate adaptive randomization designs, which are used to reduce covariate variables imbalance between treatments in clinical trials. Critical percentage and imbalance minimization methods are compared each one to another, and both are compared with pure randomization method in term of imbalance. The comparison is intended to show which method has minimum imbalance at three covariate variables with twelve single layers and three sample sizes 10, 20 and 100. The results which carried out from the simulation experiment clearly shown that the performance of critical percentage approach is closely similar to imbalance minimization method in full balance case as well as maximum imbalance at all sample sizes. And pure randomization method has the maximum imbalance compared to others at each sample size.
VL  - 2
IS  - 4
ER  -

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Author Information

Montasir Ahmed Osman

Mathematics Department, Faculty of Education, University of Kassala, Kassala, Sudan

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Plain Text BibTeX RIS

APA Style

Montasir Ahmed Osman. (2016). Covariates Adaptive Randomization Designs in Clinical Trials: A Comparative Study. International Journal of Statistical Distributions and Applications, 2(4), 72-75. https://doi.org/10.11648/j.ijsd.20160204.15

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ACS Style

Montasir Ahmed Osman. Covariates Adaptive Randomization Designs in Clinical Trials: A Comparative Study. Int. J. Stat. Distrib. Appl. 2016, 2(4), 72-75. doi: 10.11648/j.ijsd.20160204.15

Copy | Download

AMA Style

Montasir Ahmed Osman. Covariates Adaptive Randomization Designs in Clinical Trials: A Comparative Study. Int J Stat Distrib Appl. 2016;2(4):72-75. doi: 10.11648/j.ijsd.20160204.15

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@article{10.11648/j.ijsd.20160204.15,
  author = {Montasir Ahmed Osman},
  title = {Covariates Adaptive Randomization Designs in Clinical Trials: A Comparative Study},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {2},
  number = {4},
  pages = {72-75},
  doi = {10.11648/j.ijsd.20160204.15},
  url = {https://doi.org/10.11648/j.ijsd.20160204.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20160204.15},
  abstract = {This paper investigated in covariate adaptive randomization designs, which are used to reduce covariate variables imbalance between treatments in clinical trials. Critical percentage and imbalance minimization methods are compared each one to another, and both are compared with pure randomization method in term of imbalance. The comparison is intended to show which method has minimum imbalance at three covariate variables with twelve single layers and three sample sizes 10, 20 and 100. The results which carried out from the simulation experiment clearly shown that the performance of critical percentage approach is closely similar to imbalance minimization method in full balance case as well as maximum imbalance at all sample sizes. And pure randomization method has the maximum imbalance compared to others at each sample size.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Covariates Adaptive Randomization Designs in Clinical Trials: A Comparative Study
AU  - Montasir Ahmed Osman
Y1  - 2016/12/30
PY  - 2016
N1  - https://doi.org/10.11648/j.ijsd.20160204.15
DO  - 10.11648/j.ijsd.20160204.15
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 72
EP  - 75
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20160204.15
AB  - This paper investigated in covariate adaptive randomization designs, which are used to reduce covariate variables imbalance between treatments in clinical trials. Critical percentage and imbalance minimization methods are compared each one to another, and both are compared with pure randomization method in term of imbalance. The comparison is intended to show which method has minimum imbalance at three covariate variables with twelve single layers and three sample sizes 10, 20 and 100. The results which carried out from the simulation experiment clearly shown that the performance of critical percentage approach is closely similar to imbalance minimization method in full balance case as well as maximum imbalance at all sample sizes. And pure randomization method has the maximum imbalance compared to others at each sample size.
VL  - 2
IS  - 4
ER  -

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