# PipeOpPredClassifSurvIPCW

Source:`R/PipeOpPredClassifSurvIPCW.R`

`mlr_pipeops_trafopred_classifsurv_IPCW.Rd`

Transform PredictionClassif to PredictionSurv using the **I**nverse
**P**robability of **C**ensoring **W**eights (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()`

:

## 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.

## Super class

`mlr3pipelines::PipeOp`

-> `PipeOpPredClassifSurvIPCW`

## Methods

## Inherited methods

### Method `new()`

Creates a new instance of this R6 class.

#### Usage

`PipeOpPredClassifSurvIPCW$new(id = "trafopred_classifsurv_IPCW")`