parsnip_model: Fit outcome models using 'parsnip' models

In CAM-Roche/RandomisedTrialsEmulation: Causal Analysis of Observational Time-to-Event Data

Fit outcome models using parsnip models

Description

Usage

parsnip_model(model_spec, save_path)

Arguments

model_spec	A parsnip model definition with mode = "classification".
save_path	Directory to save models. Set to NA if models should not be saved.

Details

Specify that the models should be fit using a classification model specified with the parsnip package.

Warning: This functionality is experimental and not recommended for use in analyses. sqrt{n}-consistency estimation and valid inference of the parameters in marginal structural models for emulated trials generally require that the weights for treatment switching and censoring be estimated at parametric rates, which is generally not possible when using data-adaptive estimation of high-dimensional regressions. Therefore, we only recommend using stats_glm_logit().

Value

An object of class te_parsnip_model inheriting from te_model_fitter which is used for dispatching methods for the fitting models.

See Also

Other model_fitter: stats_glm_logit(), te_model_fitter-class

Examples

## Not run: 
if (
  requireNamespace("parsnip", quietly = TRUE) &&
    requireNamespace("rpart", quietly = TRUE)
) {
  # Use a decision tree model fitted with the rpart package
  parsnip_model(
    model_spec = parsnip::decision_tree(tree_depth = 30) |>
      set_mode("classification") |>
      set_engine("rpart"),
    save_path = tempdir()
  )
}

## End(Not run)

CAM-Roche/RandomisedTrialsEmulation documentation built on April 14, 2025, 7:44 a.m.