Title: Directly Standardise Rates by Age
Version: 0.2.0
Maintainer: Elin Rowlands <elin.rowlands@ndorms.ox.ac.uk>
Description: Provides functions for age standardisation of epidemiological measures such as incidence and prevalence rates. It allows users to apply standard population structures to observed age-specific estimates in order to obtain comparable summary measures across populations or time periods. Functions support calculation of standardised rates, outcome counts, and corresponding confidence intervals. The tools are designed to facilitate reproducible and transparent adjustment for differences in age distributions in epidemiological and public health research.
License: Apache License (≥ 2)
Encoding: UTF-8
RoxygenNote: 7.3.3
Suggests: gt, knitr, rmarkdown, testthat (≥ 3.0.0), DBI, duckdb, IncidencePrevalence, omopgenerics
Config/testthat/edition: 3
Imports: cli, dplyr, rlang, stringr, tidyr, tidyselect, tibble
VignetteBuilder: knitr
Depends: R (≥ 4.1.0)
URL: https://oxford-pharmacoepi.github.io/EpiStandard/, https://github.com/oxford-pharmacoepi/EpiStandard
Language: en-GB
BugReports: https://github.com/oxford-pharmacoepi/EpiStandard/issues
NeedsCompilation: no
Packaged: 2026-03-11 15:15:50 UTC; rowelin
Author: Elin Rowlands ORCID iD [aut, cre], Marta Alcalde-Herraiz ORCID iD [aut], Nuria Mercade-Besora ORCID iD [aut], Edward Burn ORCID iD [aut], Danielle Newby ORCID iD [aut]
Repository: CRAN
Date/Publication: 2026-03-24 10:00:02 UTC

EpiStandard: Directly Standardise Rates by Age

Description

Provides functions for age standardisation of epidemiological measures such as incidence and prevalence rates. It allows users to apply standard population structures to observed age-specific estimates in order to obtain comparable summary measures across populations or time periods. Functions support calculation of standardised rates, outcome counts, and corresponding confidence intervals. The tools are designed to facilitate reproducible and transparent adjustment for differences in age distributions in epidemiological and public health research.

Author(s)

Maintainer: Elin Rowlands elin.rowlands@ndorms.ox.ac.uk (ORCID)

Authors:

See Also

Useful links:

Calculate directly standardised rates

Description

Computes crude and directly standardised rates. Rates can be stratified by variables of interest.

Usage

directlyStandardiseRates(
  data,
  event,
  denominator,
  age = "age_group",
  pop = "pop",
  strata = NULL,
  addMissingGroups = TRUE,
  refdata = standardPopulation("Europe")
)

Arguments

data

A data frame with the event counts to be standardised.

event

Name of the column in data that corresponds to the event counts.

denominator

Name of the column in data that corresponds to the denominator population (in person-time, e.g person-days, person-years etc).

age

Name of the column in data and refdata that corresponds to age groups.

pop

Name of the column in refdata that corresponds to the standard population in each age group.

strata

Name of the columns in data for which rates are calculated by.

addMissingGroups

If TRUE, any age groups present in refdata but not in data will be added and set to 0. If false, these age groups will be removed from refdata.

refdata

A data frame representing the standard population. It must contain two columns: age, with the different age groups (notice that this column name must be the same as in data, defined by the input age); and pop, with the number of individuals in each corresponding age group.

Value

Data frame with crude and standardised rates.

Examples

# An example of calculating directly standardised rates
# Data example is from Table 1 (p.132) of Fundamentals of Epidemiology by Schoenbach, 2000.

# The following table shows the number of deaths, for 5 different age groups,
# in the states of Miami and Alaska:
data <- data.frame(
      state = rep(c('Miami',"Alaska"), c(5,5)),
      age_groups = rep(c('00-14','15-24','25-44','45-64','65+'),2),
      deaths = c(136, 57, 208, 1016, 3605, 59, 18, 37, 90, 81),
      general_population = c(114350,80259,133440,142670,92168,37164,20036,32693,14947,2077))

# We aim to standardise the number of deaths per each state. To do that, we will use the following
# US standard population:
standardised_population <- data.frame(
                            age_groups = c('00-14','15-24','25-44','45-64','65+'),
                            pop = c(23961000,15420000,21353000,19601000,10685000))

# Now we will use the function dsr to calculate the direct standardised rates
# (per 1000 individuals) using a 95% CI calculated by the gamma method:
my_results <- directlyStandardiseRates(data = data,
                  event = "deaths",
                  denominator  = "general_population",
                  age   = "age_groups",
                  pop   = "pop",
                  strata = "state",
                  refdata = standardised_population)
# View results
my_results

Create new merged age groups

Description

Create new merged age groups

Usage

mergeAgeGroups(
  refdata,
  newGroups,
  event = NULL,
  age = "age_group",
  pop = "pop",
  ageRange = c(0, 150),
  strata = NULL
)

Arguments

refdata

Standard population dataset you want to use.

newGroups

Create a list of new age groups you want to create.

event

Column in refdata with outcome counts.

age

Column in refdata with age values.

pop

Column in refdata with population counts, preferably in person-time.

ageRange

Specify the age range of the population of interest.

strata

Column or columns to stratify by.

Value

Data frame with age groups and population counts.

Examples




standardPopulation("Europe")|> dplyr::glimpse()

merged_data <- mergeAgeGroups(standardPopulation("Europe"), c("0-19", "20-64", "65-150"))

merged_data |> dplyr::glimpse()

Standard population

Description

Standard population

Usage

standardPopulation(region = "Europe")

Arguments

region

Region of standard population. Can be either 'Europe' or 'World'.

Value

Data frame with age groups and population counts for chosen region.

Examples


standard_data <- standardPopulation(region = "Europe")