Variable Selection for Multivariate Survival data

AUTHOR AND
AFFILIATION

1A. LOKESHMARAN* and 2R. ELANGOVAN

KEYWORDS:

Cox’s Proportional Hazards Model, Multivariate Failure Time Data, Frailty Model, Marginal Model, Nonconcave Penalized Likelihood Approach.

Issue Date:

April 2013

Pages:

ISSN:

2319-8044 (Online) – 2231-346X (Print)

Source:

Vol.25 – No.1

PDF

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DOI:

jusps-A

ABSTRACT:

It is assumed for the Cox’s proportional hazards model that the survival times of subjects are independent. This assumption might be violated in some situations, in which the collected data are correlated. The well-known Cox model is not valid in this situation because independence assumption among individuals is violated. For this purpose Cox’s proportional hazard model is extent to the analysis of multivariate failure time data, which includes frailty models and marginal model. In this paper frailty and marginal hazard models are discussed using nonconcave penalized likelihood approach. Detailed illustrations are also provided.

Copy the following to cite this Article:

1A. LOKESHMARAN* and 2R. ELANGOVAN, “Variable Selection for Multivariate Survival data”, Journal of Ultra Scientist of Physical Sciences, Volume 25, Issue 1, Page Number , 2016


Copy the following to cite this URL:

1A. LOKESHMARAN* and 2R. ELANGOVAN, “Variable Selection for Multivariate Survival data”, Journal of Ultra Scientist of Physical Sciences, Volume 25, Issue 1, Page Number , 2016

Available from: http://www.ultrascientist.org/paper/272/


It is assumed for the Cox’s proportional hazards model that the survival times of subjects are independent. This assumption might be violated in some situations, in which the collected data are correlated. The well-known Cox model is not valid in this situation because independence assumption among individuals is violated. For this purpose Cox’s proportional hazard model is extent to the analysis of multivariate failure time data, which includes frailty models and marginal model. In this paper frailty and marginal hazard models are discussed using nonconcave penalized likelihood approach. Detailed illustrations are also provided