Nothing
R/airway_05.R
In nemsqar: National Emergency Medical Service Quality Alliance Measure Calculations
Defines functions
airway_05
Documented in
airway_05
#' @title Airway-05 Calculation
#'
#' @description
#'
#' Calculates the NEMSQA Airway-05 measure.
#'
#' Calculates the proportion of endotracheal intubation attempts with adequate
#' oxygenation.
#'
#' @param df A dataframe or tibble containing EMS data where each row represents
#' an observation and columns represent features. Default is `NULL`.
#' @param patient_scene_table A data.frame or tibble containing at least
#' epatient, escene, and earrest.01 fields as a fact table. Default is `NULL`.
#' @param response_table A data.frame or tibble containing at least the
#' eresponse fields needed for this measure's calculations. Default is `NULL`.
#' @param arrest_table A data.frame or tibble containing at least the earrest
#' fields needed for this measure's calculations. Default is `NULL`.
#' @param procedures_table A dataframe or tibble containing at least the
#' eProcedures fields needed. Default is `NULL`.
#' @param vitals_table A dataframe or tibble containing at least the eVitals
#' fields needed. Default is `NULL`.
#' @param erecord_01_col The column representing the EMS record unique
#' identifier.
#' @param incident_date_col Column that contains the incident date. Default is
#' `NULL`.
#' @param patient_DOB_col Column that contains the patient's date of birth.
#' Default is `NULL`.
#' @param epatient_15_col Column representing the patient's numeric age agnostic
#' of unit.
#' @param epatient_16_col Column representing the patient's age unit ("Years",
#' "Months", "Days", "Hours", or "Minutes").
#' @param eresponse_05_col Column that contains eResponse.05.
#' @param earrest_01_col Column representing whether or not the patient is in
#' arrest.
#' @param evitals_01_col Date-time or POSIXct column containing vital signs
#' date/time
#' @param evitals_12_col Numeric column containing pulse oximetry values.
#' @param eprocedures_01_col Date-time or POSIXct column for procedures
#' @param eprocedures_02_col Column name for whether or not the procedure was
#' performed prior to EMS care being provided.
#' @param eprocedures_03_col Column containing procedure codes with or without
#' procedure names.
#' @param confidence_interval `r lifecycle::badge("experimental")` Logical. If
#' `TRUE`, the function calculates a confidence interval for the proportion
#' estimate.
#' @param method `r lifecycle::badge("experimental")`Character. Specifies the
#' method used to calculate confidence intervals. Options are `"wilson"`
#' (Wilson score interval) and `"clopper-pearson"` (exact binomial interval).
#' Partial matching is supported, so `"w"` and `"c"` can be used as shorthand.
#' @param conf.level `r lifecycle::badge("experimental")`Numeric. The confidence
#' level for the interval, expressed as a proportion (e.g., 0.95 for a 95%
#' confidence interval). Defaults to 0.95.
#' @param correct `r lifecycle::badge("experimental")`Logical. If `TRUE`,
#' applies a continuity correction to the Wilson score interval when `method =
#' "wilson"`. Defaults to `TRUE`.
#' @param ... optional additional arguments to pass onto `dplyr::summarize`.
#'
#' @return A data.frame summarizing results for two population groups (Adults
#' and Peds) with the following columns:
#' - `pop`: Population type (Adults and Peds).
#' - `numerator`: Count of incidents meeting the measure.
#' - `denominator`: Total count of included incidents.
#' - `prop`: Proportion of incidents meeting the measure.
#' - `prop_label`: Proportion formatted as a percentage with a specified number
#' of decimal places.
#' - `lower_ci`: Lower bound of the confidence interval for `prop`
#' (if `confidence_interval = TRUE`).
#' - `upper_ci`: Upper bound of the confidence interval for `prop`
#' (if `confidence_interval = TRUE`).
#'
#' @examples
#'
#' # If you are sourcing your data from a SQL database connection
#' # or if you have your data in several different tables,
#' # you can pass table inputs versus a single data.frame or tibble
#'
#' # create tables to test correct functioning
#'
#' # patient table
#' patient_table <- tibble::tibble(
#'
#' erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
#' incident_date = rep(as.Date(c("2025-01-01", "2025-01-05", "2025-02-01",
#' "2025-01-01", "2025-06-01")), 2),
#' patient_dob = rep(as.Date(c("2000-01-01", "2020-01-01", "2023-02-01",
#' "2023-01-01", "1970-06-01")), 2),
#' epatient_15 = rep(c(25, 5, 2, 2, 55), 2), # Ages
#' epatient_16 = rep(c("Years", "Years", "Years", "Years", "Years"), 2)
#'
#' )
#'
#' # response table
#' response_table <- tibble::tibble(
#'
#' erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
#' eresponse_05 = rep(2205001, 10)
#'
#' )
#'
#' # vitals table
#' vitals_table <- tibble::tibble(
#'
#' erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
#' evitals_01 = lubridate::as_datetime(c("2025-01-01 22:59:00",
#' "2025-01-05 11:58:00", "2025-02-01 18:57:00", "2025-01-01 04:58:00",
#' "2025-06-01 12:57:00", "2025-01-01 23:05:00", "2025-01-05 12:04:00",
#' "2025-02-01 19:03:00", "2025-01-01 05:02:00", "2025-06-01 13:01:00")),
#' evitals_12 = rep(c(94, 95, 96, 97, 98), 2)
#'
#' )
#'
#' # arrest table
#' arrest_table <- tibble::tibble(
#'
#' erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
#' earrest_01 = rep("No", 10)
#' )
#'
#' # procedures table
#' procedures_table <- tibble::tibble(
#'
#' erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
#' eprocedures_01 = rep(lubridate::as_datetime(c("2025-01-01 23:00:00",
#' "2025-01-05 12:00:00", "2025-02-01 19:00:00", "2025-01-01 05:00:00",
#' "2025-06-01 13:00:00")), 2),
#' eprocedures_02 = rep("No", 10),
#' eprocedures_03 = rep(c(16883004, 112798008, 78121007, 49077009,
#' 673005), 2)
#'
#' )
#'
#' # Run the function
#' # Return 95% confidence intervals using the Wilson method
#' airway_05(df = NULL,
#' patient_scene_table = patient_table,
#' procedures_table = procedures_table,
#' vitals_table = vitals_table,
#' arrest_table = arrest_table,
#' response_table = response_table,
#' erecord_01_col = erecord_01,
#' incident_date_col = incident_date,
#' patient_DOB_col = patient_dob,
#' epatient_15_col = epatient_15,
#' epatient_16_col = epatient_16,
#' eresponse_05_col = eresponse_05,
#' eprocedures_01_col = eprocedures_01,
#' eprocedures_02_col = eprocedures_02,
#' eprocedures_03_col = eprocedures_03,
#' earrest_01_col = earrest_01,
#' evitals_01_col = evitals_01,
#' evitals_12_col = evitals_12,
#' confidence_interval = TRUE
#' )
#'
#' @author Samuel Kordik, BBA, BS, Nicolas Foss Ed.D., MS
#'
#' @export
#'
airway_05 <- function(df = NULL,
patient_scene_table = NULL,
response_table = NULL,
arrest_table = NULL,
procedures_table = NULL,
vitals_table = NULL,
erecord_01_col,
incident_date_col = NULL,
patient_DOB_col = NULL,
epatient_15_col,
epatient_16_col,
earrest_01_col,
eresponse_05_col,
evitals_01_col,
evitals_12_col,
eprocedures_01_col,
eprocedures_02_col,
eprocedures_03_col,
confidence_interval = FALSE,
method = c("wilson", "clopper-pearson"),
conf.level = 0.95,
correct = TRUE,
...) {
# Set default method and adjustment method
method <- match.arg(method, choices = c("wilson", "clopper-pearson"))
# utilize applicable tables to analyze the data for the measure
if(
all(!is.null(patient_scene_table),
!is.null(response_table),
!is.null(procedures_table),
!is.null(vitals_table)
) && is.null(df)
) {
# Start timing the function execution
start_time <- Sys.time()
# header
cli::cli_h1("Airway-05")
# header
cli::cli_h2("Gathering Records for Airway-05")
#############################################################################
# #
# Get Population Level Information from tables and columns #
# #
#############################################################################
airway_05_populations <- airway_05_population(patient_scene_table = patient_scene_table,
response_table = response_table,
arrest_table = arrest_table,
procedures_table = procedures_table,
vitals_table = vitals_table,
erecord_01_col = {{ erecord_01_col }},
incident_date_col = {{ incident_date_col }},
patient_DOB_col = {{ patient_DOB_col }},
epatient_15_col = {{ epatient_15_col }},
epatient_16_col = {{ epatient_16_col }},
earrest_01_col = {{ earrest_01_col }},
eresponse_05_col = {{ eresponse_05_col }},
evitals_01_col = {{ evitals_01_col }},
evitals_12_col = {{ evitals_12_col }},
eprocedures_01_col = {{ eprocedures_01_col }},
eprocedures_02_col = {{ eprocedures_02_col }},
eprocedures_03_col = {{ eprocedures_03_col }}
)
# create a separator
cli::cli_text("\n")
# header for calculations
cli::cli_h2("Calculating Airway-05")
# summary
airway.05 <- results_summarize(
total_population = NULL,
adult_population = airway_05_populations$adults,
peds_population = airway_05_populations$peds,
measure_name = "Airway-05",
population_names = c("adults", "peds"),
numerator_col = numerator,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...
)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(airway.05$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(airway.05)
} else if(
all(is.null(patient_scene_table),
is.null(response_table),
is.null(procedures_table),
is.null(vitals_table),
is.null(arrest_table)
) && !is.null(df)
) {
# Start timing the function execution
start_time <- Sys.time()
# header
cli::cli_h1("Airway-05")
# header
cli::cli_h2("Gathering Records for Airway-05")
#############################################################################
# #
# Get Population Level Information from tables and columns #
# #
#############################################################################
airway_05_populations <- airway_05_population(df = df,
erecord_01_col = {{ erecord_01_col }},
incident_date_col = {{ incident_date_col }},
patient_DOB_col = {{ patient_DOB_col }},
epatient_15_col = {{ epatient_15_col }},
epatient_16_col = {{ epatient_16_col }},
earrest_01_col = {{ earrest_01_col }},
eresponse_05_col = {{ eresponse_05_col }},
evitals_01_col = {{ evitals_01_col }},
evitals_12_col = {{ evitals_12_col }},
eprocedures_01_col = {{ eprocedures_01_col }},
eprocedures_02_col = {{ eprocedures_02_col }},
eprocedures_03_col = {{ eprocedures_03_col }}
)
# create a separator
cli::cli_text("\n")
# header for calculations
cli::cli_h2("Calculating Airway-05")
# summary
airway.05 <- results_summarize(
adult_population = airway_05_populations$adults,
peds_population = airway_05_populations$peds,
measure_name = "Airway-05",
population_names = c("adults", "peds"),
numerator_col = numerator,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...
)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(airway.05$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(airway.05)
}
}
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Defines functions airway_05
Documented in airway_05
#' @title Airway-05 Calculation
#'
#' @description
#'
#' Calculates the NEMSQA Airway-05 measure.
#'
#' Calculates the proportion of endotracheal intubation attempts with adequate
#' oxygenation.
#'
#' @param df A dataframe or tibble containing EMS data where each row represents
#' an observation and columns represent features. Default is `NULL`.
#' @param patient_scene_table A data.frame or tibble containing at least
#' epatient, escene, and earrest.01 fields as a fact table. Default is `NULL`.
#' @param response_table A data.frame or tibble containing at least the
#' eresponse fields needed for this measure's calculations. Default is `NULL`.
#' @param arrest_table A data.frame or tibble containing at least the earrest
#' fields needed for this measure's calculations. Default is `NULL`.
#' @param procedures_table A dataframe or tibble containing at least the
#' eProcedures fields needed. Default is `NULL`.
#' @param vitals_table A dataframe or tibble containing at least the eVitals
#' fields needed. Default is `NULL`.
#' @param erecord_01_col The column representing the EMS record unique
#' identifier.
#' @param incident_date_col Column that contains the incident date. Default is
#' `NULL`.
#' @param patient_DOB_col Column that contains the patient's date of birth.
#' Default is `NULL`.
#' @param epatient_15_col Column representing the patient's numeric age agnostic
#' of unit.
#' @param epatient_16_col Column representing the patient's age unit ("Years",
#' "Months", "Days", "Hours", or "Minutes").
#' @param eresponse_05_col Column that contains eResponse.05.
#' @param earrest_01_col Column representing whether or not the patient is in
#' arrest.
#' @param evitals_01_col Date-time or POSIXct column containing vital signs
#' date/time
#' @param evitals_12_col Numeric column containing pulse oximetry values.
#' @param eprocedures_01_col Date-time or POSIXct column for procedures
#' @param eprocedures_02_col Column name for whether or not the procedure was
#' performed prior to EMS care being provided.
#' @param eprocedures_03_col Column containing procedure codes with or without
#' procedure names.
#' @param confidence_interval `r lifecycle::badge("experimental")` Logical. If
#' `TRUE`, the function calculates a confidence interval for the proportion
#' estimate.
#' @param method `r lifecycle::badge("experimental")`Character. Specifies the
#' method used to calculate confidence intervals. Options are `"wilson"`
#' (Wilson score interval) and `"clopper-pearson"` (exact binomial interval).
#' Partial matching is supported, so `"w"` and `"c"` can be used as shorthand.
#' @param conf.level `r lifecycle::badge("experimental")`Numeric. The confidence
#' level for the interval, expressed as a proportion (e.g., 0.95 for a 95%
#' confidence interval). Defaults to 0.95.
#' @param correct `r lifecycle::badge("experimental")`Logical. If `TRUE`,
#' applies a continuity correction to the Wilson score interval when `method =
#' "wilson"`. Defaults to `TRUE`.
#' @param ... optional additional arguments to pass onto `dplyr::summarize`.
#'
#' @return A data.frame summarizing results for two population groups (Adults
#' and Peds) with the following columns:
#' - `pop`: Population type (Adults and Peds).
#' - `numerator`: Count of incidents meeting the measure.
#' - `denominator`: Total count of included incidents.
#' - `prop`: Proportion of incidents meeting the measure.
#' - `prop_label`: Proportion formatted as a percentage with a specified number
#' of decimal places.
#' - `lower_ci`: Lower bound of the confidence interval for `prop`
#' (if `confidence_interval = TRUE`).
#' - `upper_ci`: Upper bound of the confidence interval for `prop`
#' (if `confidence_interval = TRUE`).
#'
#' @examples
#'
#' # If you are sourcing your data from a SQL database connection
#' # or if you have your data in several different tables,
#' # you can pass table inputs versus a single data.frame or tibble
#'
#' # create tables to test correct functioning
#'
#' # patient table
#' patient_table <- tibble::tibble(
#'
#' erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
#' incident_date = rep(as.Date(c("2025-01-01", "2025-01-05", "2025-02-01",
#' "2025-01-01", "2025-06-01")), 2),
#' patient_dob = rep(as.Date(c("2000-01-01", "2020-01-01", "2023-02-01",
#' "2023-01-01", "1970-06-01")), 2),
#' epatient_15 = rep(c(25, 5, 2, 2, 55), 2), # Ages
#' epatient_16 = rep(c("Years", "Years", "Years", "Years", "Years"), 2)
#'
#' )
#'
#' # response table
#' response_table <- tibble::tibble(
#'
#' erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
#' eresponse_05 = rep(2205001, 10)
#'
#' )
#'
#' # vitals table
#' vitals_table <- tibble::tibble(
#'
#' erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
#' evitals_01 = lubridate::as_datetime(c("2025-01-01 22:59:00",
#' "2025-01-05 11:58:00", "2025-02-01 18:57:00", "2025-01-01 04:58:00",
#' "2025-06-01 12:57:00", "2025-01-01 23:05:00", "2025-01-05 12:04:00",
#' "2025-02-01 19:03:00", "2025-01-01 05:02:00", "2025-06-01 13:01:00")),
#' evitals_12 = rep(c(94, 95, 96, 97, 98), 2)
#'
#' )
#'
#' # arrest table
#' arrest_table <- tibble::tibble(
#'
#' erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
#' earrest_01 = rep("No", 10)
#' )
#'
#' # procedures table
#' procedures_table <- tibble::tibble(
#'
#' erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
#' eprocedures_01 = rep(lubridate::as_datetime(c("2025-01-01 23:00:00",
#' "2025-01-05 12:00:00", "2025-02-01 19:00:00", "2025-01-01 05:00:00",
#' "2025-06-01 13:00:00")), 2),
#' eprocedures_02 = rep("No", 10),
#' eprocedures_03 = rep(c(16883004, 112798008, 78121007, 49077009,
#' 673005), 2)
#'
#' )
#'
#' # Run the function
#' # Return 95% confidence intervals using the Wilson method
#' airway_05(df = NULL,
#' patient_scene_table = patient_table,
#' procedures_table = procedures_table,
#' vitals_table = vitals_table,
#' arrest_table = arrest_table,
#' response_table = response_table,
#' erecord_01_col = erecord_01,
#' incident_date_col = incident_date,
#' patient_DOB_col = patient_dob,
#' epatient_15_col = epatient_15,
#' epatient_16_col = epatient_16,
#' eresponse_05_col = eresponse_05,
#' eprocedures_01_col = eprocedures_01,
#' eprocedures_02_col = eprocedures_02,
#' eprocedures_03_col = eprocedures_03,
#' earrest_01_col = earrest_01,
#' evitals_01_col = evitals_01,
#' evitals_12_col = evitals_12,
#' confidence_interval = TRUE
#' )
#'
#' @author Samuel Kordik, BBA, BS, Nicolas Foss Ed.D., MS
#'
#' @export
#'
airway_05 <- function(df = NULL,
patient_scene_table = NULL,
response_table = NULL,
arrest_table = NULL,
procedures_table = NULL,
vitals_table = NULL,
erecord_01_col,
incident_date_col = NULL,
patient_DOB_col = NULL,
epatient_15_col,
epatient_16_col,
earrest_01_col,
eresponse_05_col,
evitals_01_col,
evitals_12_col,
eprocedures_01_col,
eprocedures_02_col,
eprocedures_03_col,
confidence_interval = FALSE,
method = c("wilson", "clopper-pearson"),
conf.level = 0.95,
correct = TRUE,
...) {
# Set default method and adjustment method
method <- match.arg(method, choices = c("wilson", "clopper-pearson"))
# utilize applicable tables to analyze the data for the measure
if(
all(!is.null(patient_scene_table),
!is.null(response_table),
!is.null(procedures_table),
!is.null(vitals_table)
) && is.null(df)
) {
# Start timing the function execution
start_time <- Sys.time()
# header
cli::cli_h1("Airway-05")
# header
cli::cli_h2("Gathering Records for Airway-05")
#############################################################################
# #
# Get Population Level Information from tables and columns #
# #
#############################################################################
airway_05_populations <- airway_05_population(patient_scene_table = patient_scene_table,
response_table = response_table,
arrest_table = arrest_table,
procedures_table = procedures_table,
vitals_table = vitals_table,
erecord_01_col = {{ erecord_01_col }},
incident_date_col = {{ incident_date_col }},
patient_DOB_col = {{ patient_DOB_col }},
epatient_15_col = {{ epatient_15_col }},
epatient_16_col = {{ epatient_16_col }},
earrest_01_col = {{ earrest_01_col }},
eresponse_05_col = {{ eresponse_05_col }},
evitals_01_col = {{ evitals_01_col }},
evitals_12_col = {{ evitals_12_col }},
eprocedures_01_col = {{ eprocedures_01_col }},
eprocedures_02_col = {{ eprocedures_02_col }},
eprocedures_03_col = {{ eprocedures_03_col }}
)
# create a separator
cli::cli_text("\n")
# header for calculations
cli::cli_h2("Calculating Airway-05")
# summary
airway.05 <- results_summarize(
total_population = NULL,
adult_population = airway_05_populations$adults,
peds_population = airway_05_populations$peds,
measure_name = "Airway-05",
population_names = c("adults", "peds"),
numerator_col = numerator,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...
)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(airway.05$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(airway.05)
} else if(
all(is.null(patient_scene_table),
is.null(response_table),
is.null(procedures_table),
is.null(vitals_table),
is.null(arrest_table)
) && !is.null(df)
) {
# Start timing the function execution
start_time <- Sys.time()
# header
cli::cli_h1("Airway-05")
# header
cli::cli_h2("Gathering Records for Airway-05")
#############################################################################
# #
# Get Population Level Information from tables and columns #
# #
#############################################################################
airway_05_populations <- airway_05_population(df = df,
erecord_01_col = {{ erecord_01_col }},
incident_date_col = {{ incident_date_col }},
patient_DOB_col = {{ patient_DOB_col }},
epatient_15_col = {{ epatient_15_col }},
epatient_16_col = {{ epatient_16_col }},
earrest_01_col = {{ earrest_01_col }},
eresponse_05_col = {{ eresponse_05_col }},
evitals_01_col = {{ evitals_01_col }},
evitals_12_col = {{ evitals_12_col }},
eprocedures_01_col = {{ eprocedures_01_col }},
eprocedures_02_col = {{ eprocedures_02_col }},
eprocedures_03_col = {{ eprocedures_03_col }}
)
# create a separator
cli::cli_text("\n")
# header for calculations
cli::cli_h2("Calculating Airway-05")
# summary
airway.05 <- results_summarize(
adult_population = airway_05_populations$adults,
peds_population = airway_05_populations$peds,
measure_name = "Airway-05",
population_names = c("adults", "peds"),
numerator_col = numerator,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...
)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(airway.05$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(airway.05)
}
}
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