Predict hazards, survival and absolute risk from a fitted base learner — predict.base

Computes cause-specific piecewise-constant hazards (pwch_k), the corresponding survival function, and absolute risk for a given cause, at user-supplied prediction horizons times, using a fitted base_learner object (single learner; no stacking).

Usage

# S3 method for class 'base_learner'
predict(object, newdata, times, cause = 1, ...)

Arguments

object: base_learner. A fitted object returned by fit_learner(). It contains the learner specification in object$model and cause-specific fitted models in object$learner_fit.
newdata: data.frame/data.table. New covariate data (one row per subject). If newdata contains the original event_time, status, or id columns used for fitting, they are ignored for prediction.
times: numeric. Prediction horizon(s). May include 0. Times larger than object$data_info$maximum_followup are not supported: if all requested times exceed the maximum follow-up, a warning is issued and NULL is returned; if only some exceed, output rows for those times are returned with NA predictions.
cause: numeric(1). Cause index (1, 2, ...) used for the absolute_risk calculation.
...: Additional arguments (currently ignored).

Value

A data.table with one row per (row in newdata, time in times) and columns:

(original columns): All columns from newdata (excluding ignored event columns).
time column: A column with name object$data_info$event_time holding the requested horizon.
pwch_1, pwch_2, ...: Predicted cause-specific piecewise hazards at the horizon.
survival_function: Predicted survival probability at the horizon.
absolute_risk: Predicted cumulative incidence (absolute risk) for cause at the horizon.

Details

Internally, newdata is expanded to a Cartesian product with times, converted to long Poisson format on object$data_info$nodes, and the fitted learner for each cause in object$learner_fit is used to predict the cause-specific hazards. Survival and absolute risk are then computed from the predicted hazards.

Special case times = 0: when 0 is included in times, the returned rows have survival_function = 1, absolute_risk = 0, and all pwch_k = 0 at time 0.

Identifiers in the output: if newdata contains the id column, it is carried into the output. If newdata does not contain an id column, an internal id is created for computation, but it is not guaranteed to appear in the returned table unless it was present in newdata.

Examples

d <- simulateStenoT1(30, competing_risks = TRUE)
lrn <- Learner_glmnet(covariates = c("age", "value_LDL"), lambda = 0, cross_validation = FALSE)
bl <- fit_learner(d, learner = lrn, id="id", status="status_cvd", event_time="time_cvd",
                  number_of_nodes=8)
p <- predict(bl, newdata = d[1:5], times = c(0, 2, 5), cause = 1)
head(p)
#>       id    sex      age diabetes_duration value_SBP value_LDL value_HBA1C
#>    <int> <fctr>    <num>             <num>     <num>     <num>       <num>
#> 1:     1      0 50.75134          22.89707  137.1395  2.431660    63.32654
#> 2:     1      0 50.75134          22.89707  137.1395  2.431660    63.32654
#> 3:     1      0 50.75134          22.89707  137.1395  2.431660    63.32654
#> 4:     2      0 51.98570          24.67824  138.0197  2.349015    64.52782
#> 5:     2      0 51.98570          24.67824  138.0197  2.349015    64.52782
#> 6:     2      0 51.98570          24.67824  138.0197  2.349015    64.52782
#>    value_Smoking value_Motion value_Albuminuria      eGFR time_event_1
#>           <fctr>       <fctr>            <fctr>     <num>        <num>
#> 1:             0            1            Normal 7.3955254    0.3536507
#> 2:             0            1            Normal 7.3955254    0.3536507
#> 3:             0            1            Normal 7.3955254    0.3536507
#> 4:             0            0            Normal 0.2869973    0.8590847
#> 5:             0            0            Normal 0.2869973    0.8590847
#> 6:             0            0            Normal 0.2869973    0.8590847
#>    time_event_0 time_event_2 uncensored_time_cvd uncensored_status_cvd
#>           <num>        <num>               <num>                 <int>
#> 1:     14.58882    18.461209           0.3536507                     1
#> 2:     14.58882    18.461209           0.3536507                     1
#> 3:     14.58882    18.461209           0.3536507                     1
#> 4:     15.16226     5.201973           0.8590847                     1
#> 5:     15.16226     5.201973           0.8590847                     1
#> 6:     15.16226     5.201973           0.8590847                     1
#>         time event uncensored_time uncensored_event time_cvd    pwch_1
#>        <num> <int>           <num>            <int>    <num>     <num>
#> 1: 0.3536507     1       0.3536507                1        0 0.0000000
#> 2: 0.3536507     1       0.3536507                1        2 0.2678121
#> 3: 0.3536507     1       0.3536507                1        5 0.3629399
#> 4: 0.8590847     1       0.8590847                1        0 0.0000000
#> 5: 0.8590847     1       0.8590847                1        2 0.3699958
#> 6: 0.8590847     1       0.8590847                1        5 0.5014196
#>          pwch_2 survival_function absolute_risk
#>           <num>             <num>         <num>
#> 1: 0.000000e+00         1.0000000     0.0000000
#> 2: 1.731764e-05         0.5747452     0.4252038
#> 3: 4.498156e-01         0.1065957     0.7075264
#> 4: 0.000000e+00         1.0000000     0.0000000
#> 5: 2.523829e-05         0.4652658     0.5346641
#> 6: 6.555496e-01         0.0429922     0.7868675