Aalen Johansen Competing Risks Learner

This learner estimates the Cumulative Incidence Function (CIF) for competing risks using the empirical Aalen-Johansen (AJ) estimator.

Transition probabilities to each event are computed from the training data via the survfit function and predictions are made at all unique times (both events and censoring) observed in the training set.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

LearnerCompRisksAalenJohansen$new()
mlr_learners$get("cmprsk.aalen")
lrn("cmprsk.aalen")

Meta Information

• Task type: “cmprsk”

• Predict Types: “cif”

• Feature Types: “logical”, “integer”, “numeric”, “factor”

• Required Packages: mlr3, mlr3proba, survival

Parameters

Id	Type	Default	Levels
model	logical	FALSE	TRUE, FALSE

References

Aalen, O O, Johansen, Soren (1978). “An empirical transition matrix for non-homogeneous Markov chains based on censored observations.” Scandinavian journal of statistics, 141–150.

Super classes

mlr3::Learner -> mlr3proba::LearnerCompRisks -> LearnerCompRisksAalenJohansen

Methods

Public methods

• LearnerCompRisksAalenJohansen$new()

• LearnerCompRisksAalenJohansen$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerCompRisksAalenJohansen$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerCompRisksAalenJohansen$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3)

# Define the Learner
learner = lrn("cmprsk.aalen")
learner
#> 
#> ── <LearnerCompRisksAalenJohansen> (cmprsk.aalen): Aalen Johansen Estimator ────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3proba, and survival
#> • Predict Types: [cif]
#> • Feature Types: logical, integer, numeric, and factor
#> • Encapsulation: none (fallback: -)
#> • Properties: weights
#> • Other settings: use_weights = 'use'

# Define a Task
task = tsk("pbc")

# Stratification based on event
task$set_col_roles(cols = "status", add_to = "stratum")

# Create train and test set
part = partition(task)

# Train the learner on the training set
learner$train(task, row_ids = part$train)
learner$model
#> Call: survfit(formula = task$formula(1), data = task$data(cols = task$target_names))
#> 
#>        n nevent     rmean se(rmean)*
#> (s0) 184      0 91.573442   4.130078
#> 1    184     12  7.761548   2.157753
#> 2    184     74 49.665011   4.148925
#>    *restricted mean time in state (max time = 149 )

# Make predictions for the test set
predictions = learner$predict(task, row_ids = part$test)
predictions
#> 
#> ── <PredictionCompRisks> for 92 observations: ──────────────────────────────────
#>  row_ids time event       CIF
#>        9    1     2 <list[2]>
#>       11    9     2 <list[2]>
#>       20   22     2 <list[2]>
#>      ---  ---   ---       ---
#>      208   27     1 <list[2]>
#>      256   29     1 <list[2]>
#>      262   17     1 <list[2]>

# Score the predictions

# AUC(t = 100), weighted mean score across causes (default)
predictions$score(msr("cmprsk.auc", cause = "mean", time_horizon = 100))
#> cmprsk.auc 
#>        0.5 

# AUC(t = 100), 1st cause
predictions$score(msr("cmprsk.auc", cause = 1, time_horizon = 100))
#> cmprsk.auc 
#>        0.5 

# AUC(t = 100), 2nd cause
predictions$score(msr("cmprsk.auc", cause = 2, time_horizon = 100))
#> cmprsk.auc 
#>        0.5