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A real survival analysis application via variable selection methods for Cox's proportional hazards model

A real survival analysis application via variable selection methods for Cox's proportional hazards model
A real survival analysis application via variable selection methods for Cox's proportional hazards model
Variable selection is fundamental to high-dimensional statistical modeling in diverse fields of sciences. In our health study, different statistical methods are applied to analyze trauma annual data, collected by 30 General Hospitals in Greece. The dataset consists of 6334 observations and 111 factors that include demographic, transport, and clinical data. The statistical methods employed in this work are the nonconcave penalized likelihood methods, Smoothly Clipped Absolute Deviation, Least Absolute Shrinkage and Selection Operator, and Hard, the maximum partial likelihood estimation method, and the best subset variable selection, adjusted to Cox's proportional hazards model and used to detect possible risk factors, which affect the length of stay in a hospital. A variety of different statistical models are considered, with respect to the combinations of factors while censored observations are present. A comparative survey reveals several differences between results and execution times of each method. Finally, we provide useful biological justification of our results.
variable selection, survival analysis, cox's proportional hazards model, nonconcave penalized likelihood, high-dimensional dataset, trauma
0266-4763
1399-1406
Androulakis, Emmanouil
905b8c3e-461c-4a51-a76e-1aa545e20421
Koukouvinos, Christos
9c88d32d-b519-4d78-a60d-66418cab1926
Mylona, Kalliopi
b44af287-2d9f-4df8-931c-32d8ab117864
Vonta, Filla
f11d1da0-3fe2-4560-b2a7-b1a3c77fa581
Androulakis, Emmanouil
905b8c3e-461c-4a51-a76e-1aa545e20421
Koukouvinos, Christos
9c88d32d-b519-4d78-a60d-66418cab1926
Mylona, Kalliopi
b44af287-2d9f-4df8-931c-32d8ab117864
Vonta, Filla
f11d1da0-3fe2-4560-b2a7-b1a3c77fa581

Androulakis, Emmanouil, Koukouvinos, Christos, Mylona, Kalliopi and Vonta, Filla (2010) A real survival analysis application via variable selection methods for Cox's proportional hazards model. Journal of Applied Statistics, 37 (8), 1399-1406. (doi:10.1080/02664760903038406).

Record type: Article

Abstract

Variable selection is fundamental to high-dimensional statistical modeling in diverse fields of sciences. In our health study, different statistical methods are applied to analyze trauma annual data, collected by 30 General Hospitals in Greece. The dataset consists of 6334 observations and 111 factors that include demographic, transport, and clinical data. The statistical methods employed in this work are the nonconcave penalized likelihood methods, Smoothly Clipped Absolute Deviation, Least Absolute Shrinkage and Selection Operator, and Hard, the maximum partial likelihood estimation method, and the best subset variable selection, adjusted to Cox's proportional hazards model and used to detect possible risk factors, which affect the length of stay in a hospital. A variety of different statistical models are considered, with respect to the combinations of factors while censored observations are present. A comparative survey reveals several differences between results and execution times of each method. Finally, we provide useful biological justification of our results.

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More information

e-pub ahead of print date: 11 August 2010
Published date: 2010
Keywords: variable selection, survival analysis, cox's proportional hazards model, nonconcave penalized likelihood, high-dimensional dataset, trauma
Organisations: Statistics

Identifiers

Local EPrints ID: 336774
URI: http://eprints.soton.ac.uk/id/eprint/336774
ISSN: 0266-4763
PURE UUID: 5b8add01-627a-46fc-925d-b5004f905bff

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Date deposited: 04 Apr 2012 15:59
Last modified: 14 Mar 2024 10:46

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Contributors

Author: Emmanouil Androulakis
Author: Christos Koukouvinos
Author: Kalliopi Mylona
Author: Filla Vonta

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