Outlier Detection in Cox Proportional Hazards Models Based on the Concordance c-Index

摘要

Outliers can have extreme influence on data analysis and so their presence must be taken into account. We propose a method to perform outlier detection on multivariate survival datasets, named Dual Bootstrap Hypothesis Testing (DBHT). Experimental results show that DBHT is a competitive alternative to state-of-the-art methods and can be applied to clinical data.