Quick start guide

In tidyrates: Tidy Epidemiological Rates

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

The objective of this package is to compute rates adjusted by a reference population or other rate. This is a very common procedure in epidemiology, allowing the comparison of rates of a event (like mortality) among groups that have different age distributions.

Some packages like the epitools compute these adjusted rates. This package functions wraps the epitools functions in a tidy way, allowing the computation of age adjusted rates for several groups using key variables, like year and regions for example.

Setup

Installing the package

devtools::install_github("rfsaldanha/tidyrates")

Load the package

library(tidyrates)

Direct adjusted rate

Events and population data

Let's use the Fleiss dataset, quoted by the epitools package (Fleiss, 1981, p. 249 ).

population <- c(230061, 329449, 114920, 39487, 14208, 3052,
72202, 326701, 208667, 83228, 28466, 5375, 15050, 175702,
207081, 117300, 45026, 8660, 2293, 68800, 132424, 98301, 
46075, 9834, 327, 30666, 123419, 149919, 104088, 34392, 
319933, 931318, 786511, 488235, 237863, 61313)

population <- matrix(population, 6, 6, 
dimnames = list(c("Under 20", "20-24", "25-29", "30-34", "35-39",
"40 and over"), c("1", "2", "3", "4", "5+", "Total")))

count <- c(107, 141, 60, 40, 39, 25, 25, 150, 110, 84, 82, 39,
3, 71, 114, 103, 108, 75, 1, 26, 64, 89, 137, 96, 0, 8, 63, 112,
262, 295, 136, 396, 411, 428, 628, 530)
count <- matrix(count, 6, 6, 
dimnames = list(c("Under 20", "20-24", "25-29", "30-34", "35-39",
"40 and over"), c("1", "2", "3", "4", "5+", "Total")))

population

count

The Fleiss data present events (count object) and population (population object) for six age groups on five different groups (from 1 to 5+).

The tidyrates package present the same Fleiss data in a tidy way, with a tibble in long format.

fleiss_data

The key variable refers to the groups, age_group to the age groups, name separates the values into events and population.

You may use this same structure for your use case data.

Reference population data

The Fleiss example uses the average population as standard population reference.

standard<-apply(population[,-6], 1, mean)
standard

Using tidyrates, we must supply a tibble with two variables: age group and population.

standard_pop <- tibble::tibble(
    age_group = c("Under 20", "20-24", "25-29", "30-34", "35-39", "40 and over"),
    population = c(63986.6, 186263.6, 157302.2, 97647.0, 47572.6, 12262.6)
  )

Rate computation

To use the direct adjustment procedure, tidyrate present the rate_adj_direct function. The .data argument must be a tibble with the events and population data, and the .std argument must be standard population tibble. The .keys argument must point to grouping variables on the .data tibble, if available.

The rate_adj_direct will compute the crude rate, adjusted rate and exact confidence intervals for each group.

rate_adj_direct(fleiss_data, .std = standard_pop, .keys = "key")

Indirect adjusted rate

Events and population data

Let's use the Selvin dataset, quoted by the epitools package (Selvin, 2004).

dth40 <- c(45, 201, 320, 670, 1126, 3160, 9723, 17935,
22179, 13461, 2238)

pop40 <- c(906897, 3794573, 10003544, 10629526, 9465330,
8249558, 7294330, 5022499, 2920220, 1019504, 142532)

The tidyrates present the same dataset in a tidy way.

selvin_data_1940

Events and population reference data

dth60 <- c(141, 926, 1253, 1080, 1869, 4891, 14956, 30888,
41725, 26501, 5928)

pop60 <- c(1784033, 7065148, 15658730, 10482916, 9939972,
10563872, 9114202, 6850263, 4702482, 1874619, 330915)

The tidyrates present the same dataset in a tidy way.

selvin_data_1960

Rate computation

To use the indirect adjustment procedure, tidyrate present the rate_adj_indirect function. The .data argument must be a tibble with the events and population data, and the .std argument must be also a tibble with the events and population data. The .keys argument must point to grouping variables on the .data tibble, if available.

The rate_adj_indirect will compute the crude rate, adjusted rate and exact confidence intervals for each group.

rate_adj_indirect(selvin_data_1940, selvin_data_1960)

tidyrates documentation built on May 29, 2024, 9:49 a.m.