An Approach of Power Estimation for Linear Mixed Models for Clinical Studies

Weijia Feng

doi:doi:10.11648/j.sjams.20160402.17

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An Approach of Power Estimation for Linear Mixed Models for Clinical Studies

Weijia Feng

Published in Science Journal of Applied Mathematics and Statistics (Volume 4, Issue 2)

Received: 10 April 2016 Published: 11 April 2016

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Abstract

This study aims to demonstrate a practical way of power estimation for linear mixed models in clinical studies. Approximation methods using z and t statistics are discussed and compared to the simulated results. It was found that the approximation methods generally provide a slight overestimation of power, relative to simulated results using the Kenward and Roger estimation of degree of freedom. However, results of approximation methods can be informative in certain scenarios. In conclusion, the z approximation and t approximation with a residual degree of freedom can be useful in certain situations. Simulation method can serve as a general solution.

Published in	Science Journal of Applied Mathematics and Statistics (Volume 4, Issue 2)
DOI	10.11648/j.sjams.20160402.17
Page(s)	59-63
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

Power, Sample Size, Linear Mixed Model, Clinical Studies

References

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APA Style

Weijia Feng. (2016). An Approach of Power Estimation for Linear Mixed Models for Clinical Studies. Science Journal of Applied Mathematics and Statistics, 4(2), 59-63. https://doi.org/10.11648/j.sjams.20160402.17

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ACS Style

Weijia Feng. An Approach of Power Estimation for Linear Mixed Models for Clinical Studies. Sci. J. Appl. Math. Stat. 2016, 4(2), 59-63. doi: 10.11648/j.sjams.20160402.17

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AMA Style

Weijia Feng. An Approach of Power Estimation for Linear Mixed Models for Clinical Studies. Sci J Appl Math Stat. 2016;4(2):59-63. doi: 10.11648/j.sjams.20160402.17

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@article{10.11648/j.sjams.20160402.17,
  author = {Weijia Feng},
  title = {An Approach of Power Estimation for Linear Mixed Models for Clinical Studies},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {4},
  number = {2},
  pages = {59-63},
  doi = {10.11648/j.sjams.20160402.17},
  url = {https://doi.org/10.11648/j.sjams.20160402.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20160402.17},
  abstract = {This study aims to demonstrate a practical way of power estimation for linear mixed models in clinical studies. Approximation methods using z and t statistics are discussed and compared to the simulated results. It was found that the approximation methods generally provide a slight overestimation of power, relative to simulated results using the Kenward and Roger estimation of degree of freedom. However, results of approximation methods can be informative in certain scenarios. In conclusion, the z approximation and t approximation with a residual degree of freedom can be useful in certain situations. Simulation method can serve as a general solution.},
 year = {2016}
}

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TY  - JOUR
T1  - An Approach of Power Estimation for Linear Mixed Models for Clinical Studies
AU  - Weijia Feng
Y1  - 2016/04/11
PY  - 2016
N1  - https://doi.org/10.11648/j.sjams.20160402.17
DO  - 10.11648/j.sjams.20160402.17
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 59
EP  - 63
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20160402.17
AB  - This study aims to demonstrate a practical way of power estimation for linear mixed models in clinical studies. Approximation methods using z and t statistics are discussed and compared to the simulated results. It was found that the approximation methods generally provide a slight overestimation of power, relative to simulated results using the Kenward and Roger estimation of degree of freedom. However, results of approximation methods can be informative in certain scenarios. In conclusion, the z approximation and t approximation with a residual degree of freedom can be useful in certain situations. Simulation method can serve as a general solution.
VL  - 4
IS  - 2
ER  -

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Author Information

Weijia Feng

Alcon (China) Ophthalmic Product Co., Ltd., Beijing, China

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APA Style

Weijia Feng. (2016). An Approach of Power Estimation for Linear Mixed Models for Clinical Studies. Science Journal of Applied Mathematics and Statistics, 4(2), 59-63. https://doi.org/10.11648/j.sjams.20160402.17

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ACS Style

Weijia Feng. An Approach of Power Estimation for Linear Mixed Models for Clinical Studies. Sci. J. Appl. Math. Stat. 2016, 4(2), 59-63. doi: 10.11648/j.sjams.20160402.17

Copy | Download

AMA Style

Weijia Feng. An Approach of Power Estimation for Linear Mixed Models for Clinical Studies. Sci J Appl Math Stat. 2016;4(2):59-63. doi: 10.11648/j.sjams.20160402.17

Copy | Download

@article{10.11648/j.sjams.20160402.17,
  author = {Weijia Feng},
  title = {An Approach of Power Estimation for Linear Mixed Models for Clinical Studies},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {4},
  number = {2},
  pages = {59-63},
  doi = {10.11648/j.sjams.20160402.17},
  url = {https://doi.org/10.11648/j.sjams.20160402.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20160402.17},
  abstract = {This study aims to demonstrate a practical way of power estimation for linear mixed models in clinical studies. Approximation methods using z and t statistics are discussed and compared to the simulated results. It was found that the approximation methods generally provide a slight overestimation of power, relative to simulated results using the Kenward and Roger estimation of degree of freedom. However, results of approximation methods can be informative in certain scenarios. In conclusion, the z approximation and t approximation with a residual degree of freedom can be useful in certain situations. Simulation method can serve as a general solution.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - An Approach of Power Estimation for Linear Mixed Models for Clinical Studies
AU  - Weijia Feng
Y1  - 2016/04/11
PY  - 2016
N1  - https://doi.org/10.11648/j.sjams.20160402.17
DO  - 10.11648/j.sjams.20160402.17
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 59
EP  - 63
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20160402.17
AB  - This study aims to demonstrate a practical way of power estimation for linear mixed models in clinical studies. Approximation methods using z and t statistics are discussed and compared to the simulated results. It was found that the approximation methods generally provide a slight overestimation of power, relative to simulated results using the Kenward and Roger estimation of degree of freedom. However, results of approximation methods can be informative in certain scenarios. In conclusion, the z approximation and t approximation with a residual degree of freedom can be useful in certain situations. Simulation method can serve as a general solution.
VL  - 4
IS  - 2
ER  -

Copy | Download