PipeOpTaskSurvClassifDiscTime

Transform TaskSurv to TaskClassif by dividing continuous time into multiple time intervals for each observation. This transformation creates a new target variable disc_status that indicates whether an event occurred within each time interval. This approach facilitates survival analysis within a classification framework using discrete time intervals (Tutz et al. 2016).

Note that this data transformation is compatible with learners that support the "validation" property and can track performance on holdout data during training, enabling early stopping and logging.

Dictionary

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

PipeOpTaskSurvClassifDiscTime$new()
mlr_pipeops$get("trafotask_survclassif_disctime")
po("trafotask_survclassif_disctime")

Input and Output Channels

PipeOpTaskSurvClassifDiscTime has one input channel named "input", and two output channels, one named "output" and the other "transformed_data".

During training, the "output" is the "input" TaskSurv transformed to a TaskClassif. The target column is named "disc_status" and indicates whether an event occurred in each time interval. An additional numeric feature named "tend" contains the end time point of each interval. Lastly, the "output" task has a column with the original observation ids, under the role "original_ids". The "transformed_data" is an empty data.table.

During prediction, the "input" TaskSurv is transformed to the "output" TaskClassif with "disc_status" as target and the "tend" feature included. The "transformed_data" is a data.table with columns the "disc_status" target of the "output" task, the "id" (original observation ids), "obs_times" (observed times per "id") and "tend" (end time of each interval). This "transformed_data" is only meant to be used with the PipeOpPredClassifSurvDiscTime.

State

The $state contains information about the cut parameter used.

Parameters

The parameters are

• cut :: numeric()
  Split points, used to partition the data into intervals based on the time column. If unspecified, all unique event times will be used. If cut is a single integer, it will be interpreted as the number of equidistant intervals from 0 until the maximum event time.

• max_time :: numeric(1)
  If cut is unspecified, this will be the last possible event time. All event times after max_time will be administratively censored at max_time. Needs to be greater than the minimum event time in the given task.

References

Tutz, Gerhard, Schmid, Matthias (2016). Modeling Discrete Time-to-Event Data, series Springer Series in Statistics. Springer International Publishing. ISBN 978-3-319-28156-8 978-3-319-28158-2, http://link.springer.com/10.1007/978-3-319-28158-2.

See also

pipeline_survtoclassif_disctime

Other Transformation PipeOps: mlr_pipeops_trafopred_classifsurv_IPCW, mlr_pipeops_trafopred_classifsurv_disctime, mlr_pipeops_trafopred_regrsurv_pem, mlr_pipeops_trafotask_survclassif_IPCW, mlr_pipeops_trafotask_survregr_pem

Super class

mlr3pipelines::PipeOp -> PipeOpTaskSurvClassifDiscTime

Methods

Public methods

• PipeOpTaskSurvClassifDiscTime$new()

• PipeOpTaskSurvClassifDiscTime$clone()

Inherited methods

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

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Method new()

Creates a new instance of this R6 class.

Usage

PipeOpTaskSurvClassifDiscTime$new(id = "trafotask_survclassif_disctime")

Arguments

id

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

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Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpTaskSurvClassifDiscTime$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) { # \dontrun{
  library(mlr3)
  library(mlr3learners)
  library(mlr3pipelines)

  task = tsk("lung")

  # transform the survival task to a classification task
  # all unique event times are used as cutpoints
  po_disc = po("trafotask_survclassif_disctime")
  task_classif = po_disc$train(list(task))[[1L]]

  # the end time points of the discrete time intervals
  unique(task_classif$data(cols = "tend"))[[1L]]

  # train a classification learner
  learner = lrn("classif.log_reg", predict_type = "prob")
  learner$train(task_classif)
} # }