A mixture Cox-Logistic model for feature selection from survival and classification data
This work addresses the need for integrated analysis in biomedical research, such as cancer studies, by providing a method that simultaneously handles survival and classification data, though it is incremental as it builds on existing Cox and logistic models.
The paper tackles the problem of jointly modeling survival times and classifying samples into subgroups by proposing the Coxlogit model, which uses a common set of features for both tasks and shows improved performance in predicting survival and classification on synthetic and breast cancer data compared to standard Cox or logistic regression models.
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.