Fine-Gray Competing Risks Learner

Fine-Gray subdistribution hazards model for competing risks using cmprsk::crr().

Details

The fitted model is an S3 object of class "fine_gray" that stores a cause-specific list of crr class models.

At prediction time, each cause-specific model is evaluated with cmprsk::predict.crr() and the resulting CIF matrices are aligned to a common time grid across causes using constant CIF interpolation. The time grid is the unique event times (across all causes) observed in the training set.

Time-interaction terms (via cov2) are not implemented.

Dictionary

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

mlr_learners$get("cmprsk.fg")
lrn("cmprsk.fg")

Meta Information

• Task type: “cmprsk”

• Predict Types: “cif”

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

• Required Packages: mlr3, mlr3cmprsk, cmprsk

Parameters

Id	Type	Default	Levels	Range
cengroup	numeric	-		\((-\infty, \infty)\)
gtol	numeric	1e-06		\([0, \infty)\)
maxiter	integer	10		\([0, \infty)\)
init	untyped	-		-
variance	logical	TRUE	TRUE, FALSE	-

References

Fine, P J, Gray, J R (1999). “A Proportional Hazards Model for the Subdistribution of a Competing Risk.” Journal of the American Statistical Association, 94(446), 496–509. doi:10.1080/01621459.1999.10474144 .

See also

Other competing risk learners: mlr_learners_cmprsk.aalen

Super classes

mlr3::Learner -> mlr3cmprsk::LearnerCompRisks -> LearnerCompRisksFineGray

Methods

Public methods

• LearnerCompRisksFineGray$new()

• LearnerCompRisksFineGray$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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerCompRisksFineGray$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerCompRisksFineGray$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("cmprsk.fg")
learner
#> 
#> ── <LearnerCompRisksFineGray> (cmprsk.fg): Competing Risks Regression: Fine-Gray
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3cmprsk, and cmprsk
#> • Predict Types: [cif]
#> • Feature Types: logical, integer, and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties:
#> • Other settings: use_weights = 'error', predict_raw = 'FALSE'

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

# Subset task features as Fine-Gray model doesn't accept factors
# Encode factors with `mlr3pipelines::po("encode")` if needed
feats = c("age", "chol", "albumin", "ast", "bili", "protime")
task$select(feats)

# 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$native_model
#> $`1`
#> convergence:  TRUE 
#> coefficients:
#>        age    albumin        ast       bili       chol    protime 
#> -0.0861400  0.0224300 -0.0018270  0.0065160  0.0009787 -0.4471000 
#> standard errors:
#> [1] 0.026500 0.910600 0.004680 0.135600 0.001879 0.541600
#> two-sided p-values:
#>     age albumin     ast    bili    chol protime 
#>  0.0012  0.9800  0.7000  0.9600  0.6000  0.4100 
#> 
#> $`2`
#> convergence:  TRUE 
#> coefficients:
#>        age    albumin        ast       bili       chol    protime 
#>  0.0430700 -1.3450000  0.0029910  0.1708000 -0.0008802  0.1901000 
#> standard errors:
#> [1] 0.0169100 0.2684000 0.0020410 0.0293800 0.0004506 0.1091000
#> two-sided p-values:
#>     age albumin     ast    bili    chol protime 
#> 1.1e-02 5.4e-07 1.4e-01 6.2e-09 5.1e-02 8.1e-02 
#> 
#> attr(,"class")
#> [1] "fine_gray"

# Make predictions for the test set
predictions = learner$predict(task, row_ids = part$test)
predictions
#> 
#> ── <PredictionCompRisks> for 92 observations: ──────────────────────────────────
#>  row_ids time event       CIF
#>        4   63     2 <list[2]>
#>        8   78     2 <list[2]>
#>        9    1     2 <list[2]>
#>      ---  ---   ---       ---
#>      139   81     1 <list[2]>
#>      157   73     1 <list[2]>
#>      231   35     1 <list[2]>

# Score the predictions
# AUC(t = 100), weighted mean score across causes (default)
predictions$score(msr("cmprsk.auc", cause = "mean", time = 100))
#> cmprsk.auc 
#>  0.7353124 

# AUC(t = 100), with user-specified weights
predictions$score(msr("cmprsk.auc", cause = "mean", cause_weights = c(0.2, 0.8),
  time = 100))
#> cmprsk.auc 
#>   0.741607 

# AUC(t = 100), 1st cause
predictions$score(msr("cmprsk.auc", cause = 1, time = 100))
#> cmprsk.auc 
#>  0.8248884 

# AUC(t = 100), 2nd cause
predictions$score(msr("cmprsk.auc", cause = 2, time = 100))
#> cmprsk.auc 
#>  0.7207866 

# Prediction error (Brier score) at specific time point
# BS(t = 100) => weighted mean score across causes (default)
predictions$score(msr("cmprsk.brier", time = 100))
#> cmprsk.brier 
#>    0.1758196 

# BS(t = 100), 1st cause
predictions$score(msr("cmprsk.brier", cause = 1, time = 100))
#> cmprsk.brier 
#>   0.06342861 

# BS(t = 100), 2nd cause
predictions$score(msr("cmprsk.brier", cause = 2, time = 100))
#> cmprsk.brier 
#>    0.1940452