Calculate directly standardised rates

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
#> # A tibble: 2 × 9
#>   state  deaths general_population crude_rate crude_rate_95CI_lower
#>   <chr>   <dbl>              <dbl>      <dbl>                 <dbl>
#> 1 Miami    5022             562887       892.                  868.
#> 2 Alaska    285             106917       267.                  236.
#> # ℹ 4 more variables: crude_rate_95CI_upper <dbl>, standardised_rate <dbl>,
#> #   standardised_rate_95CI_lower <dbl>, standardised_rate_95CI_upper <dbl>