rate_adj_indirect {tidyrates}

Compute direct adjusted rates with tibbles

Description

Computes indirect adjusted rates and confidence intervals.

Usage

rate_adj_indirect(
  .data,
  .std,
  .keys = NULL,
  .name_var = "name",
  .value_var = "value",
  .age_group_var = "age_group",
  .age_group_pop_var = "population",
  .events_label = "events",
  .population_label = "population",
  .progress = TRUE
)

Arguments

.data

A tibble containing events counts and population per groups (e.g. age groups)

.std

A vector with standard population values for each group

.keys

Optional. A character vector with grouping variables, like year and region code.

.name_var

Variable containing variable names. Defaults to name.

.value_var

Variable containing values. Defaults to value.

.age_group_var

Variable name of age groups. Defaults to age_group.

.age_group_pop_var

Variable name of population size on .std. Defaults to population.

.events_label

Label used for events at the name_var variable. Defaults to events.

.population_label

Label used for population at the name_var variable. Defautls to population.

.progress

Whether to show a progress bar. Defaults to TRUE.

Details

This functions wraps the epitools ageadjust.indirect function to compute indirect adjusted rates and "exact" confidence intervals using tibble objects with multiple grouping keys.

A tibble (.data) must be informed containing key variables like year and region code, and population and and events count (e.g. cases) per age group. Check the fleiss_data for an example.

A tibble (.std) must be also supplied containing the age groups, events and population size. By default, this tibble has three variables, named age_group, name and value. Check the selvin_data_1940 for an example.

Value

A tibble with crude and adjusted rate, lower and upper confidence intervals.

Examples

rate_adj_indirect(.data = selvin_data_1960, .std = selvin_data_1940)