Compute Pairwise Co-resistance Using Pearson Back-calculation

For each antibiotic-class pair (A, B) and each pathogen [x stratum], this function:

1. Computes the Pearson correlation \(\rho_{AB}\) between binary resistance indicators across co-tested isolates.

2. Back-calculates pairwise prevalence using the GBD formula: $$P(A \cap B) = P(A) P(B) + \rho_{AB} \sqrt{P(A)(1-P(A)) \cdot P(B)(1-P(B))}$$ where \(P(A)\) and \(P(B)\) come from marginals (which may include externally overridden values).

3. Caps the result to \(\min(P(A), P(B))\) and floors it to 0. Cells below the floor use the independence product as fallback.

Usage

compute_pairwise_from_data(
  data_wide,
  marginals,
  col_map,
  panel_map,
  stratify_by = NULL,
  outcome_col = NULL,
  min_co_tested = 10L
)

Arguments

data_wide

Tibble. Output of preprocess_for_profiles()$data_wide.

marginals

Tibble. Output of compute_marginals_from_data().

col_map

Named list. col_map_resolved.

panel_map

Named list. Same panel as used in preprocessing.

stratify_by

Character vector or NULL. Must match what was used in compute_marginals_from_data(). Default NULL.

outcome_col

Character or NULL. Default NULL.

min_co_tested

Integer. Pairs with fewer co-tested isolates are reported with method = "independence_fallback". Default 10L.

Value

Tibble with one row per pathogen x class-pair [x stratum]:

pathogen, antibiotic_class_1, antibiotic_class_2

Keys.

n_co_tested

Isolates tested for both classes.

rho

Pearson correlation between binary resistance indicators.

pairwise_prevalence

Back-calculated P(AnB), capped and floored.

method

"pearson_back_calc" or "independence_fallback".