A mixture Cox-Logistic model for feature selection from survival and classification data

TL;DR

This paper introduces a Cox-Logistic model that jointly predicts survival times and classifies samples into subgroups using shared features, improving accuracy and feature selection over traditional methods.

Contribution

The paper proposes a novel joint modeling approach combining survival analysis and classification with a shared feature set, estimated via regularized likelihood maximization.

Findings

Outperforms standard Cox and logistic models in prediction accuracy.

Better at selecting informative features for both tasks.

Validated on synthetic and breast cancer data.

Abstract

This paper presents an original approach for jointly fitting survival times and classifying samples into subgroups. The Coxlogit model is a generalized linear model with a common set of selected features for both tasks. Survival times and class labels are here assumed to be conditioned by a common risk score which depends on those features. Learning is then naturally expressed as maximizing the joint probability of subgroup labels and the ordering of survival events, conditioned to a common weight vector. The model is estimated by minimizing a regularized log-likelihood through a coordinate descent algorithm. Validation on synthetic and breast cancer data shows that the proposed approach outperforms a standard Cox model or logistic regression when both predicting the survival times and classifying new samples into subgroups. It is also better at selecting informative features for both tasks.