Androulakis, Emmanouil, Koukouvinos, Christos, Mylona, Kalliopi and Vonta, Filla
A real survival analysis application via variable selection methods for Cox's proportional hazards model
Journal of Applied Statistics, 37, (8), . (doi:10.1080/02664760903038406).
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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.
|Digital Object Identifier (DOI):
||variable selection, survival analysis, cox's proportional hazards model, nonconcave penalized likelihood, high-dimensional dataset, trauma
|11 August 2010||e-pub ahead of print|
||04 Apr 2012 15:59
||17 Apr 2017 17:21
|Further Information:||Google Scholar|
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