Dear biostats community,
I am trying to build a Cox (Proportional Hazards) Regression and have a dataset with several variables. I am trying to decide which variables are the most useful to use as covariates in my model. Which method do you use and recommend for doing this? I thought at first to do a univariate analysis and see which variables don't have a significant survival difference to exclude them but as I understand such procedures have the issue that they don't take into account the interaction between the different variables.
Thank you very much!!
Gabriel
Hi,
Intuitively selecting based upon already associated covariates from literature. another way is step-wise selection.
This is a reference link for different methods which can be used.:
https://www.diva-portal.org/smash/get/diva2:1067479/FULLTEXT01.pdf
and
Garcia, R. I., Ibrahim, J. G., & Zhu, H. (2010). Variable selection in the cox regression model with covariates missing at random. Biometrics, 66(1), 97–104. https://doi.org/10.1111/j.1541-0420.2009.01274.x
For a theoretical discussion:
Handbook of Survival Analysis by John P. Klein, CRC Press Taylor & Francis Group
Stepwise would be a terrible choice. I recommend lasso or the adaptive lasso version. See the attached papers. Best wishes, David Booth
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