Application of Cox Regression and Kaplan Meir Estimates in the Survival Rate of Patients

Amos Langat; Joel Koima

doi:doi:10.11648/j.jb.20170102.11

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Application of Cox Regression and Kaplan Meir Estimates in the Survival Rate of Patients

Amos Langat, Joel Koima

Published in Journal of Biomaterials (Volume 1, Issue 2)

Received: 10 April 2017 Accepted: 28 April 2017 Published: 3 July 2017

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Abstract

This study aim at focusing on the survival analysis for human subjects, to compare efficacy and safety, controlled experiments which conducted as clinical trials. Sometime it is interesting to compare the survival of subjects in two or more interventions. In situations where survival is the issue then the variable of interest would be the length of time that elapses before some event to occur. In many of the situations this length of time is very long for example in cancer therapy; in such case per unit duration of time number of events such as death can be assessed. The paper is highlighting the two difference estimates in the survival distribution of patients and later explain the strengths of the two estimates when use simultaneously in estimating the survival distribution. The researchers found that, application of the two estimates; Cox regression and Kaplan Meir will result in minimum errors estimates thus producing sufficient and complete survival distribution of patients under study.

Published in	Journal of Biomaterials (Volume 1, Issue 2)
DOI	10.11648/j.jb.20170102.11
Page(s)	29-33
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), 2017. Published by Science Publishing Group

Keywords

Survival Analysis, Cox Regression, Kaplan Meir

References

[1]	Armitage P, Berry G, Matthews JN. 4th ed. Oxford (UK): Blackwell Science; 2002. Clinical trials. Statistical methods in medical research; p. 591
[2]	Berwick V, Cheek L, Ball J. Statistics review 12: Survival analysis. Crit Care. 2004; 8: 389–94
[3]	Altman DG. London (UK): Chapman and Hall; 1992. Analysis of Survival times. In: Practical statistics for Medical research; pp. 365–93
[4]	Cox DR, Oakes D. Analysis of Survival Data, Chapman and Hall, 1984
[5]	Hosmer, DW and Lemeshow, S. Applied Survival Analysis: Regression Modeling of Time to Event Data. New York: John Wiley and Sons; 1999
[6]	Altman DG, De Stavola BL, Love SB, Stepniewska KA (1995) Review of survival analyses published in cancer journals. Br J Cancer 72:511–518
[7]	Carter RE, Huang P. Cautionary note regarding the use of CIs obtained from Kaplan-Meier survival curves. J Clin Oncol 2009; 27:174-5
[8]	Rich JT, Neely JG, Paniello RC, Voelker CC, Nussenbaum B, Wang EW. A practical guide to understanding Kaplan-Meier curves. Otolaryngol Head Neck Surg 2010;143:331-6
[9]	Cox D. Regression Models and Life-Tables. Journal of the Royal Statistical Society, Series B. 1972;34:187–220
[10]	Kaplan E, Meier P. Nonparametric Estimation from Incomplete Observations. J Am Stat Assoc. 1958; 53:457–81. doi: 10.2307/2281868
[11]	GEHAN EA. A generalized Wilcoxon test for comparing arbitrarily singly-censored samples. Biometrika. 1965; 52:203–23
[12]	Mantel N. Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemother Rep. 1966; 50:163–70

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APA Style

Amos Langat, Joel Koima. (2017). Application of Cox Regression and Kaplan Meir Estimates in the Survival Rate of Patients. Journal of Biomaterials, 1(2), 29-33. https://doi.org/10.11648/j.jb.20170102.11

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ACS Style

Amos Langat; Joel Koima. Application of Cox Regression and Kaplan Meir Estimates in the Survival Rate of Patients. J. Biomater. 2017, 1(2), 29-33. doi: 10.11648/j.jb.20170102.11

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AMA Style

Amos Langat, Joel Koima. Application of Cox Regression and Kaplan Meir Estimates in the Survival Rate of Patients. J Biomater. 2017;1(2):29-33. doi: 10.11648/j.jb.20170102.11

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@article{10.11648/j.jb.20170102.11,
  author = {Amos Langat and Joel Koima},
  title = {Application of Cox Regression and Kaplan Meir Estimates in the Survival Rate of Patients},
  journal = {Journal of Biomaterials},
  volume = {1},
  number = {2},
  pages = {29-33},
  doi = {10.11648/j.jb.20170102.11},
  url = {https://doi.org/10.11648/j.jb.20170102.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jb.20170102.11},
  abstract = {This study aim at focusing on the survival analysis for human subjects, to compare efficacy and safety, controlled experiments which conducted as clinical trials. Sometime it is interesting to compare the survival of subjects in two or more interventions. In situations where survival is the issue then the variable of interest would be the length of time that elapses before some event to occur. In many of the situations this length of time is very long for example in cancer therapy; in such case per unit duration of time number of events such as death can be assessed. The paper is highlighting the two difference estimates in the survival distribution of patients and later explain the strengths of the two estimates when use simultaneously in estimating the survival distribution. The researchers found that, application of the two estimates; Cox regression and Kaplan Meir will result in minimum errors estimates thus producing sufficient and complete survival distribution of patients under study.},
 year = {2017}
}

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TY  - JOUR
T1  - Application of Cox Regression and Kaplan Meir Estimates in the Survival Rate of Patients
AU  - Amos Langat
AU  - Joel Koima
Y1  - 2017/07/03
PY  - 2017
N1  - https://doi.org/10.11648/j.jb.20170102.11
DO  - 10.11648/j.jb.20170102.11
T2  - Journal of Biomaterials
JF  - Journal of Biomaterials
JO  - Journal of Biomaterials
SP  - 29
EP  - 33
PB  - Science Publishing Group
SN  - 2640-2629
UR  - https://doi.org/10.11648/j.jb.20170102.11
AB  - This study aim at focusing on the survival analysis for human subjects, to compare efficacy and safety, controlled experiments which conducted as clinical trials. Sometime it is interesting to compare the survival of subjects in two or more interventions. In situations where survival is the issue then the variable of interest would be the length of time that elapses before some event to occur. In many of the situations this length of time is very long for example in cancer therapy; in such case per unit duration of time number of events such as death can be assessed. The paper is highlighting the two difference estimates in the survival distribution of patients and later explain the strengths of the two estimates when use simultaneously in estimating the survival distribution. The researchers found that, application of the two estimates; Cox regression and Kaplan Meir will result in minimum errors estimates thus producing sufficient and complete survival distribution of patients under study.
VL  - 1
IS  - 2
ER  -

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Author Information

Amos Langat

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Joel Koima

Department of Mathematics and Informatics, Kabarak University, Nakuru, Kenya

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APA Style

Amos Langat, Joel Koima. (2017). Application of Cox Regression and Kaplan Meir Estimates in the Survival Rate of Patients. Journal of Biomaterials, 1(2), 29-33. https://doi.org/10.11648/j.jb.20170102.11

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ACS Style

Amos Langat; Joel Koima. Application of Cox Regression and Kaplan Meir Estimates in the Survival Rate of Patients. J. Biomater. 2017, 1(2), 29-33. doi: 10.11648/j.jb.20170102.11

Copy | Download

AMA Style

Amos Langat, Joel Koima. Application of Cox Regression and Kaplan Meir Estimates in the Survival Rate of Patients. J Biomater. 2017;1(2):29-33. doi: 10.11648/j.jb.20170102.11

Copy | Download

@article{10.11648/j.jb.20170102.11,
  author = {Amos Langat and Joel Koima},
  title = {Application of Cox Regression and Kaplan Meir Estimates in the Survival Rate of Patients},
  journal = {Journal of Biomaterials},
  volume = {1},
  number = {2},
  pages = {29-33},
  doi = {10.11648/j.jb.20170102.11},
  url = {https://doi.org/10.11648/j.jb.20170102.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jb.20170102.11},
  abstract = {This study aim at focusing on the survival analysis for human subjects, to compare efficacy and safety, controlled experiments which conducted as clinical trials. Sometime it is interesting to compare the survival of subjects in two or more interventions. In situations where survival is the issue then the variable of interest would be the length of time that elapses before some event to occur. In many of the situations this length of time is very long for example in cancer therapy; in such case per unit duration of time number of events such as death can be assessed. The paper is highlighting the two difference estimates in the survival distribution of patients and later explain the strengths of the two estimates when use simultaneously in estimating the survival distribution. The researchers found that, application of the two estimates; Cox regression and Kaplan Meir will result in minimum errors estimates thus producing sufficient and complete survival distribution of patients under study.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Application of Cox Regression and Kaplan Meir Estimates in the Survival Rate of Patients
AU  - Amos Langat
AU  - Joel Koima
Y1  - 2017/07/03
PY  - 2017
N1  - https://doi.org/10.11648/j.jb.20170102.11
DO  - 10.11648/j.jb.20170102.11
T2  - Journal of Biomaterials
JF  - Journal of Biomaterials
JO  - Journal of Biomaterials
SP  - 29
EP  - 33
PB  - Science Publishing Group
SN  - 2640-2629
UR  - https://doi.org/10.11648/j.jb.20170102.11
AB  - This study aim at focusing on the survival analysis for human subjects, to compare efficacy and safety, controlled experiments which conducted as clinical trials. Sometime it is interesting to compare the survival of subjects in two or more interventions. In situations where survival is the issue then the variable of interest would be the length of time that elapses before some event to occur. In many of the situations this length of time is very long for example in cancer therapy; in such case per unit duration of time number of events such as death can be assessed. The paper is highlighting the two difference estimates in the survival distribution of patients and later explain the strengths of the two estimates when use simultaneously in estimating the survival distribution. The researchers found that, application of the two estimates; Cox regression and Kaplan Meir will result in minimum errors estimates thus producing sufficient and complete survival distribution of patients under study.
VL  - 1
IS  - 2
ER  -

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