Introduction
This vignette illustrates how EpiStandard can be used to
standardise results from the IncidencePrevalence package, a
DARWIN-EU package for estimating incidence and prevalence from data
mapped to the OMOP Common Data Model.
Set-up
We first load the relevant packages and then create a mock OMOP-formatted dataset containing a population and an outcome of interest.
library(EpiStandard)
library(IncidencePrevalence)
library(omopgenerics)
library(dplyr)
cdm <- mockIncidencePrevalence(
sampleSize = 10000,
outPre = 0.25
)Estimate Incidence Rates
We use the IncidencePrevalence package to generate a denominator cohort with age and sex stratifications, and then calculate overall outcome incidence and incidence by calendar year for each stratum combination. For more information on how to use this package, refer to its website.
Notice that the results are stored as a summarised_result object.
cdm <- generateDenominatorCohortSet(
cdm = cdm,
name = "denominator",
cohortDateRange = as.Date(c("2008-01-01", "2020-01-01")),
ageGroup = list(c(0, 19), c(20, 64), c(65, 150), c(0, 150)),
sex = c("Male", "Female"),
daysPriorObservation = 0
)
inc <- estimateIncidence(
cdm = cdm,
denominatorTable = "denominator",
outcomeTable = "outcome",
interval = c("years", "overall"),
outcomeWashout = 0,
repeatedEvents = FALSE
)
inc |> glimpse()
#> Rows: 1,049
#> Columns: 13
#> $ result_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ cdm_name <chr> "mock", "mock", "mock", "mock", "mock", "mock", "mock…
#> $ group_name <chr> "denominator_cohort_name &&& outcome_cohort_name", "d…
#> $ group_level <chr> "denominator_cohort_8 &&& cohort_1", "denominator_coh…
#> $ strata_name <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name <chr> "Denominator", "Outcome", "Denominator", "Denominator…
#> $ variable_level <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ estimate_name <chr> "denominator_count", "outcome_count", "person_days", …
#> $ estimate_type <chr> "integer", "integer", "numeric", "numeric", "numeric"…
#> $ estimate_value <chr> "277", "14", "85765", "234.812", "5962.217", "3259.59…
#> $ additional_name <chr> "incidence_start_date &&& incidence_end_date &&& anal…
#> $ additional_level <chr> "2008-01-01 &&& 2008-12-31 &&& years", "2008-01-01 &&…To use EpiStandard, we first filter the incidence
results to the age groups of interest for standardisation and then
convert the results into a format that facilitates further
manipulation.
Standardise Incidence Rates
incidenceTidy <- inc |>
filterSettings(denominator_age_group %in% c("0 to 19", "20 to 64", "65 to 150")) |>
asIncidenceResult()
incidenceTidy |> glimpse()
#> Rows: 69
#> Columns: 26
#> $ cdm_name <chr> "mock", "mock", "mock", "mock", "…
#> $ denominator_cohort_name <chr> "denominator_cohort_2", "denomina…
#> $ outcome_cohort_name <chr> "cohort_1", "cohort_1", "cohort_1…
#> $ incidence_start_date <date> 2008-01-01, 2009-01-01, 2010-01-…
#> $ incidence_end_date <date> 2008-12-31, 2009-12-31, 2010-12-…
#> $ analysis_interval <chr> "years", "years", "years", "years…
#> $ analysis_censor_cohort_name <chr> "None", "None", "None", "None", "…
#> $ analysis_complete_database_intervals <chr> "TRUE", "TRUE", "TRUE", "TRUE", "…
#> $ analysis_outcome_washout <chr> "0", "0", "0", "0", "0", "0", "0"…
#> $ analysis_repeated_events <chr> "FALSE", "FALSE", "FALSE", "FALSE…
#> $ denominator_age_group <chr> "0 to 19", "0 to 19", "0 to 19", …
#> $ denominator_days_prior_observation <chr> "0", "0", "0", "0", "0", "0", "0"…
#> $ denominator_end_date <date> 2020-01-01, 2020-01-01, 2020-01-…
#> $ denominator_requirements_at_entry <chr> "FALSE", "FALSE", "FALSE", "FALSE…
#> $ denominator_sex <chr> "Female", "Female", "Female", "Fe…
#> $ denominator_start_date <date> 2008-01-01, 2008-01-01, 2008-01-…
#> $ denominator_target_cohort_name <chr> "None", "None", "None", "None", "…
#> $ denominator_time_at_risk <chr> "0 to Inf", "0 to Inf", "0 to Inf…
#> $ denominator_count <int> 38, 44, 38, 23, 16, 14, 8, 7, 4, …
#> $ outcome_count <int> 0, 1, 1, 0, 1, 0, 0, 0, 0, 3, 3, …
#> $ person_days <dbl> 12529, 12754, 10404, 7081, 5510, …
#> $ person_years <dbl> 34.303, 34.919, 28.485, 19.387, 1…
#> $ incidence_100000_pys <dbl> 0.000, 2863.770, 3510.620, 0.000,…
#> $ incidence_100000_pys_95CI_lower <dbl> 0.000, 72.504, 88.881, 0.000, 167…
#> $ incidence_100000_pys_95CI_upper <dbl> 10753.810, 15955.908, 19559.921, …
#> $ result_type <chr> "tidy_incidence", "tidy_incidence…Now that the incidence results are in the correct format, we prepare
the reference population. In this example, we use the European Standard
Population (esp2013). As this population uses 5-year age
bands, we merge these groups to match those used to estimate
incidence.
standardPop <- mergeAgeGroups(
standardPopulation("Europe"),
newGroups = c("0 to 19", "20 to 64", "65 to 150")
) |>
rename("denominator_age_group" = "age_group")
standardPop |> glimpse()
#> Rows: 3
#> Columns: 2
#> $ denominator_age_group <chr> "0 to 19", "20 to 64", "65 to 150"
#> $ pop <int> 21500, 59000, 19500Finally, we use the directlyStandardiseRates() function
to standardise the incidence results to the reference population. We
specify the columns containing the event counts, person-years, and
population weights, as well as the column identifying age groups. Since
we want to standardise within each outcome, sex, and calendar-time
stratum, we use the strata argument as follows:
standardInc <- directlyStandardiseRates(
data = incidenceTidy,
refdata = standardPop,
event = "outcome_count",
denominator = "person_years",
age = "denominator_age_group",
pop = "pop",
strata = c("incidence_start_date", "denominator_sex", "analysis_interval", "outcome_cohort_name")
)
standardInc |> glimpse()
#> Rows: 22
#> Columns: 12
#> $ incidence_start_date <date> 2009-01-01, 2010-01-01, 2012-01-01, 2008…
#> $ denominator_sex <chr> "Female", "Female", "Female", "Female", "…
#> $ analysis_interval <chr> "years", "years", "years", "overall", "ye…
#> $ outcome_cohort_name <chr> "cohort_1", "cohort_1", "cohort_1", "coho…
#> $ outcome_count <int> 10, 14, 4, 51, 13, 14, 9, 13, 4, 5, 71, 1…
#> $ person_years <dbl> 233.057, 209.706, 132.954, 1298.001, 272.…
#> $ crude_rate <dbl> 4290.7958, 6676.0131, 3008.5594, 3929.118…
#> $ crude_rate_95CI_lower <dbl> 1631.3819, 3178.9678, 60.2254, 2850.7726,…
#> $ crude_rate_95CI_upper <dbl> 6950.210, 10173.058, 5956.893, 5007.465, …
#> $ standardised_rate <dbl> 4276.532, 6868.422, 3078.205, 3860.587, 5…
#> $ standardised_rate_95CI_lower <dbl> 1527.5585, 3158.1231, -362.0191, 2744.249…
#> $ standardised_rate_95CI_upper <dbl> 7025.506, 10578.720, 6518.429, 4976.924, …