An online framework for survival analysis: reframing Cox proportional hazards model for large data sets and neural networks.

ABSTRACT: In many biomedical applications, outcome is measured as a "time-to-event" (e.g., disease progression or death). To assess the connection between features of a patient and this outcome, it is common to assume a proportional hazards model and fit a proportional hazards regression (or Cox regression). To fit this model, a log-concave objective function known as the "partial likelihood" is maximized. For moderate-sized data sets, an efficient Newton-Raphson algorithm that leverages the structure of the objective function can be employed. However, in large data sets this approach has two issues: (i) The computational tricks that leverage structure can also lead to computational instability; (ii) The objective function does not naturally decouple: Thus, if the data set does not fit in memory, th

SUBMITTER: Tarkhan A 

PROVIDER: S-EPMC10724274 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature