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PipeOpPredClassifSurvIPCW

Source: R/PipeOpPredClassifSurvIPCW.R
mlr_pipeops_trafopred_classifsurv_IPCW.Rd

Transform PredictionClassif to PredictionSurv using the Inverse Probability of Censoring Weights (IPCW) method by Vock et al. (2016).

Dictionary

This PipeOp can be instantiated via the dictionary mlr3pipelines::mlr_pipeops or with the associated sugar function mlr3pipelines::po():

PipeOpPredClassifSurvIPCW$new()
mlr_pipeops$get("trafopred_classifsurv_IPCW")
po("trafopred_classifsurv_IPCW")

Input and Output Channels

The input is a PredictionClassif and a data.table containing observed times, censoring indicators and row ids, all generated by PipeOpTaskSurvClassifIPCW during the prediction phase.

The output is the input PredictionClassif transformed to a PredictionSurv. Each input classification probability prediction corresponds to the probability of having the event up to the specified cutoff time \(\hat{\pi}(\bold{X}_i) = P(T_i < \tau|\bold{X}_i)\), see Vock et al. (2016) and PipeOpTaskSurvClassifIPCW. Therefore, these predictions serve as continuous risk scores that can be directly interpreted as crank predictions in the right-censored survival setting. We also map them to the survival distribution prediction distr, at the specified cutoff time point \(\tau\), i.e. as \(S_i(\tau) = 1 - \hat{\pi}(\bold{X}_i)\). Survival measures that use the survival distribution (eg ISBS) should be evaluated exactly at the cutoff time point \(\tau\), see example.

References

Vock, M D, Wolfson, Julian, Bandyopadhyay, Sunayan, Adomavicius, Gediminas, Johnson, E P, Vazquez-Benitez, Gabriela, O'Connor, J P (2016). “Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting.” Journal of Biomedical Informatics, 61, 119–131. doi:10.1016/j.jbi.2016.03.009 , https://www.sciencedirect.com/science/article/pii/S1532046416000496.

See also

pipeline_survtoclassif_IPCW

Other Transformation PipeOps: mlr_pipeops_trafopred_classifsurv_disctime, mlr_pipeops_trafotask_survclassif_IPCW, mlr_pipeops_trafotask_survclassif_disctime

Super class

mlr3pipelines::PipeOp -> PipeOpPredClassifSurvIPCW

Active bindings

predict_type

(character(1))
Returns the active predict type of this PipeOp, which is "crank"

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

PipeOpPredClassifSurvIPCW$new(id = "trafopred_classifsurv_IPCW")

Arguments

id

(character(1))
Identifier of the resulting object.

Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpPredClassifSurvIPCW$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.