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Model Selection and Interpretation

  • Dirk F. Moore
Chapter
Part of the Use R! book series (USE R)

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.

Keywords

Likelihood Ratio Test Baseline Hazard Smoothing Spline Partial Likelihood Potential Explanatory Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Dirk F. Moore
    • 1
  1. 1.Department of BiostatisticsRutgers School of Public HealthPiscatawayUSA

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