mlr_pipeops_trafopred_classifsurv_IPCW: PipeOpPredClassifSurvIPCW

In mlr-org/mlr3proba: Probabilistic Supervised Learning for 'mlr3'

PipeOpPredClassifSurvIPCW

Description

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.

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

• PipeOpPredClassifSurvIPCW$new()

• PipeOpPredClassifSurvIPCW$clone()

Inherited methods

• mlr3pipelines::PipeOp$help()
• mlr3pipelines::PipeOp$predict()
• mlr3pipelines::PipeOp$print()
• mlr3pipelines::PipeOp$train()

---

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.

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/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_trafopred_regrsurv_pem, mlr_pipeops_trafotask_survclassif_IPCW, mlr_pipeops_trafotask_survclassif_disctime, mlr_pipeops_trafotask_survregr_pem

mlr-org/mlr3proba documentation built on April 12, 2025, 4:38 p.m.