A review of survival stacking: a method to cast survival regression analysis as a classification problem.

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Erin Craig, Chenyang Zhong, Robert Tibshirani

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Erin Craig: Department of Biomedical Data Science, 6429 Stanford University , Stanford, USA. ORCID
Chenyang Zhong: Department of Statistics, Columbia University, New York, USA.
Robert Tibshirani: Departments of Biomedical Data Science and Statistics, 6429 Stanford University , Stanford, USA.

PMID: 40147074 DOI: 10.1515/ijb-2022-0055

While there are many well-developed data science methods for classification and regression, there are relatively few methods for working with right-censored data. Here, we review survival stacking, a method for casting a survival regression analysis problem as a classification problem, thereby allowing the use of general classification methods and software in a survival setting. Inspired by the Cox partial likelihood, survival stacking collects features and outcomes of survival data in a large data frame with a binary outcome. We show that survival stacking with logistic regression is approximately equivalent to the Cox proportional hazards model. We further illustrate survival stacking on real and simulated data. By reframing survival regression problems as classification problems, survival stacking removes the reliance on specialized tools for survival regression, and makes it straightforward for data scientists to use well-known learning algorithms and software for classification in the survival setting. This in turn lowers the barrier for flexible survival modeling.

Cox regression censored data survival regression analysis

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