summarize_measure: Summarize Measure

Description

Calculates measure numerator, denominator, proportions, and optional confidence intervals for a NEMSQA measure. This function summarizes the information for a specified population and measure, returning a tibble with the calculated values. If requested, the function can also calculate confidence intervals for the proportions using either the Wilson score interval or the Clopper-Pearson exact binomial interval.

Usage

summarize_measure(
  data,
  measure_name,
  population_name,
  numerator_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Value

A summarized data frame containing:

measure: The measure name.
pop: The population group.
numerator: The count of qualifying events.
denominator: The total count of records.
prop: The proportion of qualifying events.
prop_label: A formatted percentage representation of prop (when confidence_interval = FALSE).
lower_ci, upper_ci: The lower and upper confidence interval bounds (when confidence_interval = TRUE).

Arguments

data: A dataframe or tibble containing the filtered and calculated fields for the population of interest.
measure_name: A string containing the description of the measure being calculated (e.g., "Airway-01").
population_name: A string containing the description of the population for which the measure is being calculated (e.g., "Adults", "Peds", or "All").
numerator_col: The tidyselect column containing the numerator data for the measure (e.g., the number of cases).
confidence_interval: A logical value indicating whether to calculate a confidence interval for the proportion estimate. Defaults to FALSE.
method: A string specifying the method to calculate the confidence intervals. Options are "wilson" (Wilson score interval) or "clopper-pearson" (exact binomial interval). Partial matching is allowed (e.g., "w" or "c"). Default is "wilson".
conf.level: A numeric value indicating the confidence level for the interval, expressed as a proportion (e.g., 0.95 for a 95% confidence interval). Defaults to 0.95.
correct: A logical value specifying whether to apply continuity correction to the Wilson score interval when method = "wilson". Default is TRUE.
...: (optional) Additional arguments passed to nemsqa_binomial_confint when calculating confidence intervals.

Author

Samuel Kordik, BBA, BS