Volume 1, Issue 2, December 2017, Page: 29-33
Application of Cox Regression and Kaplan Meir Estimates in the Survival Rate of Patients
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
Received: Apr. 10, 2017;       Accepted: Apr. 28, 2017;       Published: Jul. 3, 2017
DOI: 10.11648/j.jb.20170102.11      View  1413      Downloads  46
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.
Survival Analysis, Cox Regression, Kaplan Meir
To cite this article
Amos Langat, Joel Koima, Application of Cox Regression and Kaplan Meir Estimates in the Survival Rate of Patients, Journal of Biomaterials. Vol. 1, No. 2, 2017, pp. 29-33. doi: 10.11648/j.jb.20170102.11
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Armitage P, Berry G, Matthews JN. 4th ed. Oxford (UK): Blackwell Science; 2002. Clinical trials. Statistical methods in medical research; p. 591
Berwick V, Cheek L, Ball J. Statistics review 12: Survival analysis. Crit Care. 2004; 8: 389–94
Altman DG. London (UK): Chapman and Hall; 1992. Analysis of Survival times. In: Practical statistics for Medical research; pp. 365–93
Cox DR, Oakes D. Analysis of Survival Data, Chapman and Hall, 1984
Hosmer, DW and Lemeshow, S. Applied Survival Analysis: Regression Modeling of Time to Event Data. New York: John Wiley and Sons; 1999
Altman DG, De Stavola BL, Love SB, Stepniewska KA (1995) Review of survival analyses published in cancer journals. Br J Cancer 72:511–518
Carter RE, Huang P. Cautionary note regarding the use of CIs obtained from Kaplan-Meier survival curves. J Clin Oncol 2009; 27:174-5
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
Cox D. Regression Models and Life-Tables. Journal of the Royal Statistical Society, Series B. 1972;34:187–220
Kaplan E, Meier P. Nonparametric Estimation from Incomplete Observations. J Am Stat Assoc. 1958; 53:457–81. doi: 10.2307/2281868
GEHAN EA. A generalized Wilcoxon test for comparing arbitrarily singly-censored samples. Biometrika. 1965; 52:203–23
Mantel N. Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemother Rep. 1966; 50:163–70
Browse journals by subject