Abstract
Survival analysis studies typically include a wealth of clinical, demographic, and biomarker information on the patients as well as indicators for a therapy or other intervention. If the study is a randomized clinical trial, the focus will be on comparing the effectiveness of different treatments. A successful randomization procedure should ensure that confounding covariates are balanced between the treatments. Still, we may wish to include such covariates in the model to adjust for any differences that may have arisen, and also to understand how these other factors affect survival. If the study is based on observational data, and if there is a primary intervention of interest, then adjustment for potential confounders is essential to obtaining a valid estimate of the intervention effect. The effect of other covariates on survival will also be of interest in such a study, and in some applications discovery and quantification of explanatory variables may be the primary goal. Regardless of the type of study, we will need methods to sift through a potentially large number of potential explanatory variables to find the important ones.
The original version of this chapter was revised. An erratum to this chapter can be found at DOI 10.1007/978-3-319-31245-3_13
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-31245-3_13
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
De Boor, C.: A Practical Guide to Splines. Revised edition (1994)
Harrell, F.E.: Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis, 2nd edn. Springer Science & Business Media, New York (2015)
Hosmer, D.W., Jr., Lemeshow, S., May, S.: Applied Survival Analysis: Regression Modeling of Time to Event Data. Wiley, Hoboken (2008)
Li, L., Yan, J., Xu, J., Liu, C.-Q., Zhen, Z.-J., Chen, H.-W., Ji, Y., Wu, Z.-P., Hu, J.-Y., Zheng, L., et al.: CXCL17 expression predicts poor prognosis and correlates with adverse immune infiltration in hepatocellular carcinoma. PloS One 9(10), e110064 (2014)
Li, L., Yan, J., Xu, J., Liu, C.-Q., Zhen, Z.-J., Chen, H.-W., Ji, Y., Wu, Z.-P., Hu, J.-Y., Zheng, L., et al.: Data from: CXCL17 expression predicts poor prognosis and correlates with adverse immune infiltration in hepatocellular carcidata. Dryad Digital Repository, http://datadryad.org (2014)
Therneau, T.M., Grambsch, P.M.: Modeling Survival Data: Extending the Cox Model. Springer, New York (2000)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Moore, D.F. (2016). Model Selection and Interpretation. In: Applied Survival Analysis Using R. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-319-31245-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-31245-3_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31243-9
Online ISBN: 978-3-319-31245-3
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)