Bias Plus Variance Decomposition for Survival Analysis Problems

TL;DR

This paper extends bias-variance decomposition to survival analysis, comparing Cox regression and CoxPath, revealing that CoxPath does not always outperform Cox regression.

Contribution

Introduces bias-variance decomposition for survival analysis and empirically compares Cox regression and CoxPath algorithms.

Findings

CoxPath does not always outperform Cox regression.

Bias-variance analysis provides insights into algorithm performance.

Experiments conducted on increasing training set sizes.

Abstract

Bias - variance decomposition of the expected error defined for regression and classification problems is an important tool to study and compare different algorithms, to find the best areas for their application. Here the decomposition is introduced for the survival analysis problem. In our experiments, we study bias -variance parts of the expected error for two algorithms: original Cox proportional hazard regression and CoxPath, path algorithm for L1-regularized Cox regression, on the series of increased training sets. The experiments demonstrate that, contrary expectations, CoxPath does not necessarily have an advantage over Cox regression.