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Expand a cohort data.frame and attach pre-computed weights, in memory (wrapper)

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

Frame-in / frame-out analogue of expand_trial_weighted(): takes an in-memory cohort data.frame and a pre-computed factor data.frame (id, period, weight_factor), and returns the weighted, expanded frame as a data.frame. Wraps the extendr-generated expand_weighted_df(). A bit64::integer64 id in either frame round-trips exactly.

Usage

expand_trial_weighted_df(
  cohort,
  factors,
  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"
)

Arguments

cohort

A data.frame (or tibble / data.table / Arrow Table) of long person-time rows. Coerced with as.data.frame().

factors

A data.frame with columns id, period, weight_factor. Coerced with as.data.frame().

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"; selects the weight model upstream, but the application arithmetic is identical for both.

Value

A data.frame with the six structural columns plus weight.

See also

expand_trial_weighted() for the Parquet-path equivalent.

Examples

cohort <- data.frame(
  id = c(1L, 1L, 1L, 2L, 2L), period = c(0L, 1L, 2L, 0L, 1L),
  treatment = c(1L, 1L, 0L, 0L, 1L), eligible = c(1L, 0L, 0L, 1L, 0L),
  outcome = c(0L, 0L, 1L, 0L, 0L)
)
factors <- data.frame(
  id = c(1L, 1L, 1L, 2L, 2L), period = c(0L, 1L, 2L, 0L, 1L),
  weight_factor = c(1, 0.9, 1.05, 1, 1.1)
)
expand_trial_weighted_df(cohort, factors, estimand = "PP")
#>   id trial_period followup_time assigned_treatment treatment outcome weight
#> 1  1            0             0                  1         1       0    1.0
#> 2  1            0             1                  1         1       0    0.9
#> 3  2            0             0                  0         0       0    1.0