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Fit IPW weights and expand a cohort into a weighted trial frame (ergonomic wrapper)

Source: R/tters-package.R
expand_trial_weighted_fitted.Rd

User-facing wrapper around the extendr-generated expand_weighted_fitted_parquet(). It takes a raw person-time cohort straight to a weighted, expanded trial frame in one call — fitting the switching and/or IPCW models in Rust (no pre-computed factor table), expanding under estimand, and accumulating the fitted factor into the cumulative weight. The six structural columns are bit-exact; weight matches the Oracle within the staged ~1e-6 tolerance. Robust/sandwich variance and the marginal structural model stay in R.

Usage

expand_trial_weighted_fitted(
  input_path,
  output_path,
  id_col = "id",
  period_col = "period",
  treatment_col = "treatment",
  eligible_col = "eligible",
  outcome_col = "outcome",
  first_period = 0L,
  last_period = .Machine$integer.max,
  estimand = "PP",
  switch_numerator = NULL,
  switch_denominator = NULL,
  censor_col = NULL,
  censor_numerator = NULL,
  censor_denominator = NULL,
  pool_censor = "none"
)

Arguments

input_path

Path to an existing input Parquet cohort (long person-time).

output_path

Path to write the weighted, expanded Parquet.

id_col, period_col, treatment_col, eligible_col, outcome_col

Column names. Defaults match the TrialEmulation conventions.

first_period, last_period

Inclusive integer period bounds.

estimand

"ITT" or "PP". Per-protocol runs the artificial-censoring state machine and the switching models; intention-to-treat skips both.

switch_numerator, switch_denominator

Character vectors of covariate column names for the switching numerator (stabiliser) / denominator models, or NULL (the default) to omit switching weights.

censor_col

Name of the {0,1} censoring-indicator column; the modelled response is 1 - censor_col. NULL (the default) omits IPCW weights.

censor_numerator, censor_denominator

Character vectors of covariate column names for the IPCW numerator / denominator models.

pool_censor

How the IPCW models are pooled across the previous-treatment strata: "none", "numerator", or "both".

Value

output_path, invisibly.

Details

Model presence follows the same rule as fit_trial_weights(): a switching model is fitted when either switch_* covariate vector is non-NULL; an IPCW model is fitted when censor_col is non-NULL.

See also

fit_trial_weights() to write only the (id, period, weight_factor) factor table.

Examples

# Raw cohort straight to a weighted, expanded frame in one call:
input <- system.file("extdata", "weights", "input_data_censored.parquet",
                     package = "tters")
expand_trial_weighted_fitted(input, tempfile(fileext = ".parquet"),
                             estimand = "PP", switch_numerator = "x2",
                             switch_denominator = c("x2", "x1"))