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Calculates scores for assessing the relationship between individual features and the time-to-event outcome (right-censored survival data) using a univariate Cox proportional hazards model. The goal is to determine which features have a statistically significant association with the event of interest, typically in the context of clinical or biomedical research.

This filter fits a Cox Proportional Hazards model using each feature independently and extracts the \(p\)-value that quantifies the significance of the feature's impact on survival. The filter value is -log10(p) where p is the \(p\)-value. This transformation is necessary to ensure numerical stability for very small \(p\)-values. Also higher values denote more important features. The filter works only for numeric features so please ensure that factor variables are properly encoded, e.g. using PipeOpEncode.

Super class

mlr3filters::Filter -> FilterUnivariateCox


Inherited methods

Method new()

Create a FilterUnivariateCox object.


Method clone()

The objects of this class are cloneable with this method.


FilterUnivariateCox$clone(deep = FALSE)



Whether to make a deep clone.


filter = flt("univariate_cox")
#> <FilterUnivariateCox:surv.univariate_cox>: Univariate Cox Survival Score
#> Task Types: surv
#> Properties: -
#> Task Properties: -
#> Packages: survival
#> Feature types: integer, numeric, logical