Predict hazards, survival and absolute risk from a fitted Poisson Super Learner

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, for each row in newdata.

Usage

# S3 method for class 'poisson_superlearner'
predict(object, newdata, times, cause = 1, model = "sl", ...)

Arguments

object

poisson_superlearner. A fitted ensemble from Superlearner().

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.

model

Model selector. Default is "sl" for the stacked super learner. Allowed values are:

0, "sl", "superlearner", or "super_learner"

Use the stacked super learner prediction. For causes with only one retained learner or no fitted meta-learner, this falls back to the retained base learner for that cause.

"discrete_sl" and aliases

For each cause, use the retained base learner with the smallest cross-validated deviance.

learner label

Use one stored base learner by its label in object$data_info$learners_labels[[k]].

"learner_j" or character integer "j"

Use the j-th stored learner.

integer j >= 1

Use the j-th stored learner.

vector of labels or positive integer indices

Use cause-specific base learners; length must equal object$data_info$n_crisks.

Numeric positions refer to the learners actually retained for each cause in the fitted object.

...

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 the requested times, converted to long Poisson format on object$data_info$nodes, and hazards are predicted either from the stacked super learner (model = "sl"), the discrete super learner (model = "discrete_sl"), or selected fitted base learners. 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)

learners <- list(
  lasso = Learner_glmnet(
    covariates = "sex",
    alpha = 1,
    lambda = 0.01,
    cross_validation = FALSE
  ),
  ridge = Learner_glmnet(
    covariates = c("sex", "value_LDL"),
    alpha = 0,
    lambda = 0.01,
    cross_validation = FALSE
  )
)

fit <- Superlearner(
  data = d,
  id = "id",
  status = "status_cvd",
  event_time = "time_cvd",
  learners = learners,
  number_of_nodes = 3,
  nfold = 2
)
p <- predict(fit, newdata = d[1:3], times = c(0, 2), cause = 1)
p[, .(id, time_cvd, absolute_risk)]
#>       id time_cvd absolute_risk
#>    <int>    <num>         <num>
#> 1:     1        0    0.00000000
#> 2:     1        2    0.05184506
#> 3:     2        0    0.00000000
#> 4:     2        2    0.09291898
#> 5:     3        0    0.00000000
#> 6:     3        2    0.07529414