PipeOpTaskSurvRegr

Description

Transform TaskSurv to TaskRegr.

Input and Output Channels

Input and output channels are inherited from PipeOpTaskTransformer.

The output is the input TaskSurv transformed to a TaskRegr.

State

The $state is a named list with the $state elements

• instatus: Censoring status from input training task.

• outstatus : Censoring status from input prediction task.

Parameters

The parameters are

• method::character(1))
  Method to use for dealing with censoring. Options are "ipcw" (Vock et al., 2016): censoring is column is removed and a weights column is added, weights are inverse estimated survival probability of the censoring distribution evaluated at survival time; "mrl" (Klein and Moeschberger, 2003): survival time of censored observations is transformed to the observed time plus the mean residual life-time at the moment of censoring; "bj" (Buckley and James, 1979): Buckley-James imputation assuming an AFT model form, calls bujar::bujar; "delete": censored observations are deleted from the data-set - should be used with caution if censoring is informative; "omit": the censoring status column is deleted - again should be used with caution; "reorder": selects features and targets and sets the target in the new task object. Note that "mrl" and "ipcw" will perform worse with Type I censoring.

• estimator::(character(1))
  Method for calculating censoring weights or mean residual lifetime in "mrl", current options are: "kaplan": unconditional Kaplan-Meier estimator; "akritas": conditional non-parameteric nearest-neighbours estimator; "cox".

• alpha::(numeric(1))
  When ipcw is used, optional hyper-parameter that adds an extra penalty to the weighting for censored observations. If set to 0 then censored observations are given zero weight and deleted, weighting only the non-censored observations. A weight for an observation is then (δ + α(1-δ))/G(t) where δ is the censoring indicator.

• eps::numeric(1)
  Small value to replace 0 survival probabilities with in IPCW to prevent infinite weights.

• lambda::(numeric(1))
  Nearest neighbours parameter for the "akritas" estimator in the mlr3extralearners package, default 0.5.

• features, target :: character())
  For "reorder" method, specify which columns become features and targets.

• learner cneter, mimpu, iter.bj, max.cycle, mstop, nu
  Passed to bujar::bujar.

Super classes

mlr3pipelines::PipeOp -> mlr3proba::PipeOpTransformer -> mlr3proba::PipeOpTaskTransformer -> PipeOpTaskSurvRegr

Methods

Public methods

• PipeOpTaskSurvRegr$new()

• PipeOpTaskSurvRegr$clone()

Method new()

Creates a new instance of this R6 class.

Usage

PipeOpTaskSurvRegr$new(id = "trafotask_survregr", param_vals = list())

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpTaskSurvRegr$clone(deep = FALSE)

Arguments

References

Buckley, Jonathan, James, Ian (1979). “Linear Regression with Censored Data.” Biometrika, 66(3), 429–436. doi: 10.2307/2335161, https://www.jstor.org/stable/2335161.

Klein, P J, Moeschberger, L M (2003). Survival analysis: techniques for censored and truncated data, 2 edition. Springer Science & Business Media. ISBN 0387216456.

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

Other PipeOps: PipeOpPredTransformer, PipeOpTaskTransformer, PipeOpTransformer, mlr_pipeops_survavg, mlr_pipeops_trafopred_regrsurv, mlr_pipeops_trafopred_survregr, mlr_pipeops_trafotask_regrsurv

Other Transformation PipeOps: mlr_pipeops_trafopred_regrsurv, mlr_pipeops_trafopred_survregr, mlr_pipeops_trafotask_regrsurv

Examples

## Not run: 
if (requireNamespace("mlr3pipelines", quietly = TRUE)) {
  library(mlr3)
  library(mlr3pipelines)

  # these methods are generally only successful if censoring is not too high
  # create survival task by undersampling
  task = tsk("rats")$filter(
    c(which(tsk("rats")$truth()[, 2] == 1),
      sample(which(tsk("rats")$truth()[, 2] == 0), 42))
  )

  # deletion
  po = po("trafotask_survregr", method = "delete")
  po$train(list(task, NULL))[[1]] # 42 deleted

  # omission
  po = po("trafotask_survregr", method = "omit")
  po$train(list(task, NULL))[[1]]

  if (requireNamespace("mlr3extralearners", quietly = TRUE)) {
    # ipcw with Akritas
    po = po("trafotask_survregr", method = "ipcw", estimator = "akritas", lambda = 0.4, alpha = 0)
    new_task = po$train(list(task, NULL))[[1]]
    print(new_task)
    new_task$weights
  }

  # mrl with Kaplan-Meier
  po = po("trafotask_survregr", method = "mrl")
  new_task = po$train(list(task, NULL))[[1]]
  data.frame(new = new_task$truth(), old = task$truth())

  # Buckley-James imputation
  if (requireNamespace("bujar", quietly = TRUE)) {
    po = po("trafotask_survregr", method = "bj")
    new_task = po$train(list(task, NULL))[[1]]
    data.frame(new = new_task$truth(), old = task$truth())
  }

  # reorder - in practice this will be only be used in a few graphs
  po = po("trafotask_survregr", method = "reorder", features = c("sex", "rx", "time", "status"),
    target = "litter")
  new_task = po$train(list(task, NULL))[[1]]
  print(new_task)

  # reorder using another task for feature names
  po = po("trafotask_survregr", method = "reorder", target = "litter")
  new_task = po$train(list(task, task))[[1]]
  print(new_task)
}

## End(Not run)