A Cox Model refers to the Cox proportional hazards regression model, a statistical method commonly used in survival analysis. It models the relationship between individuals’ survival time and one or more predictor variables. The Cox model estimates the hazard function, which represents the instantaneous risk of an event (e.g., death, failure, or relapse) occurring at a given time, conditional on the individual having survived up to that time.

The model is widely used in clinical and biomedical research due to its ability to handle censored data (when the exact time of the event is unknown for some individuals) and to provide interpretable hazard ratios for the predictors.

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Subgroup Discovery with the Cox Model

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We study the problem of subgroup discovery with Cox regression models and introduce a method for finding an interpretable subset of the data on which a Cox model is highly accurate. Our method relies on two technical innovations: the emph (Unknown sysvar: (expected prediction entropy)), a novel metric for evaluating survival models which predict a hazard function; and the emph (Unknown sysvar: (conditional rank distribution)), a statistical object which quantifies the deviation of an individual point to the distribution of survival times in an existing subgroup. Because of the interpretability of the discovered subgroups, in addition to improving the predictive accuracy of the model, they can also form meaningful, data-driven patient cohorts for further study in a clinical setting.

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