Calculates expected counts and Standardised Mortality Ratios (SMR) using a global reference rate derived from the input dataset.
Usage
risk_calc_smr(
data,
observed_col = "event_count",
denominator_col,
expected_col = "expected_count",
smr_col = "smr",
global_rate_col = NULL,
ci_method = c("exact", "none"),
conf_level = 0.95,
smr_lower_col = "smr_lower",
smr_upper_col = "smr_upper",
smr_ci_flag_col = "smr_ci_flag",
zero_expected_value = NA_real_
)Arguments
- data
A data frame or sf object.
- observed_col
Name of observed event count column.
- denominator_col
Name of denominator, population, or exposure column.
- expected_col
Name of expected count output column.
- smr_col
Name of SMR output column.
- global_rate_col
Name of global reference rate output column.
- ci_method
Confidence interval method. Currently supports
"exact".- conf_level
Confidence level for intervals.
- smr_lower_col
Name of lower confidence interval column.
- smr_upper_col
Name of upper confidence interval column.
- smr_ci_flag_col
Name of SMR interpretation/classification column.
- zero_expected_value
Value returned when expected count is missing, zero, or negative.
Details
Optional confidence intervals are calculated using Poisson-based methods, appropriate for rare-event and small-area analysis.
References
Poisson confidence interval approaches commonly used in epidemiological small-area and rare-event analysis.
#' @examples data <- data.frame( event_count = c(5, 10, 20), population = c(1000, 2000, 3000) )
risk_calc_smr( data = data, observed_col = "event_count", denominator_col = "population" )