Help for package IncidencePrevalence

Title:

Estimate Incidence and Prevalence using the OMOP Common Data Model

Version:

1.2.0

Description:

Calculate incidence and prevalence using data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Incidence and prevalence can be estimated for the total population in a database or for a stratification cohort.

Encoding:

UTF-8

RoxygenNote:

7.3.2

Depends:

R (≥ 4.1)

Imports:

CDMConnector (≥ 2.0.0), cli (≥ 3.0.0), clock, dplyr (≥ 1.1.0), glue (≥ 1.5.0), omopgenerics (≥ 1.1.0), magrittr (≥ 2.0.0), PatientProfiles (≥ 1.3.1), purrr (≥ 0.3.5), rlang (≥ 1.0.0), stringr (≥ 1.5.0), tidyr (≥ 1.2.0)

Suggests:

knitr, dbplyr, rmarkdown, RPostgres, duckdb (≥ 1.0.0), DBI (≥ 1.0.0), odbc, here, Hmisc, epitools, tictoc, testthat (≥ 0.3.1), spelling, gt, flextable, ggplot2 (≥ 3.4.0), scales (≥ 1.1.0), visOmopResults (≥ 1.0.2), patchwork, binom

Config/testthat/edition:

Config/testthat/parallel:

true

VignetteBuilder:

knitr

Language:

en-US

License:

Apache License (≥ 2)

URL:

https://darwin-eu.github.io/IncidencePrevalence/

LazyData:

true

NeedsCompilation:

Packaged:

2025-03-08 09:23:03 UTC; eburn

Author:

Edward Burn

[aut, cre], Berta Raventos

[aut], Marti Catala

[aut], Mike Du

[ctb], Yuchen Guo

[ctb], Adam Black

[ctb], Ger Inberg

[ctb], Kim Lopez

[ctb]

Maintainer:

Edward Burn <edward.burn@ndorms.ox.ac.uk>

Repository:

CRAN

Date/Publication:

2025-03-08 10:10:02 UTC

IncidencePrevalence: Estimate Incidence and Prevalence using the OMOP Common Data Model

Description

Author(s)

Maintainer: Edward Burn edward.burn@ndorms.ox.ac.uk (ORCID)

Authors:

Berta Raventos braventos@idiapjgol.info (ORCID)
Marti Catala marti.catalasabate@ndorms.ox.ac.uk (ORCID)

Other contributors:

Mike Du mike.du@ndorms.ox.ac.uk (ORCID) [contributor]
Yuchen Guo yuchen.guo@ndorms.ox.ac.uk (ORCID) [contributor]
Adam Black black@ohdsi.org (ORCID) [contributor]
Ger Inberg g.inberg@erasmusmc.nl (ORCID) [contributor]
Kim Lopez kim.lopez@spc.ox.ac.uk (ORCID) [contributor]

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

lhs

A value or the magrittr placeholder.

rhs

A function call using the magrittr semantics.

Value

The result of calling rhs(lhs).

Benchmarking results

Description

Benchmarking results

Usage

IncidencePrevalenceBenchmarkResults

Format

A list of results from benchmarking

A tidy implementation of the summarised_result object for incidence results.

Description

A tidy implementation of the summarised_result object for incidence results.

Usage

asIncidenceResult(result, metadata = FALSE)

Arguments

result

A summarised_result object created by the IncidencePrevalence package.

metadata

If TRUE additional metadata columns will be included in the result.

Value

A tibble with a tidy version of the summarised_result object.

Examples


cdm <- mockIncidencePrevalence()
inc <- estimateIncidence(cdm, "target", "outcome")
tidy_inc <- asIncidenceResult(inc)

A tidy implementation of the summarised_result object for prevalence results.

Description

A tidy implementation of the summarised_result object for prevalence results.

Usage

asPrevalenceResult(result, metadata = FALSE)

Arguments

result

A summarised_result object created by the IncidencePrevalence package.

metadata

If TRUE additional metadata columns will be included in the result.

Value

A tibble with a tidy version of the summarised_result object.

Examples


cdm <- mockIncidencePrevalence()
prev <- estimatePointPrevalence(cdm, "target", "outcome")
tidy_prev <- asPrevalenceResult(prev)

Variables that can be used for faceting and colouring incidence plots

Description

Variables that can be used for faceting and colouring incidence plots

Usage

availableIncidenceGrouping(result, varying = FALSE)

Arguments

result

Incidence results

varying

If FALSE, only variables with non-unique values will be returned, otherwise all available variables will be returned

Examples


cdm <- mockIncidencePrevalence(sampleSize = 1000)
cdm <- generateDenominatorCohortSet(
  cdm = cdm, name = "denominator",
  cohortDateRange = c(as.Date("2014-01-01"), as.Date("2018-01-01"))
)
inc <- estimateIncidence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome"
)
availableIncidenceGrouping(inc)

Variables that can be used for faceting and colouring prevalence plots

Description

Variables that can be used for faceting and colouring prevalence plots

Usage

availablePrevalenceGrouping(result, varying = FALSE)

Arguments

result

Prevalence results

varying

If FALSE, only variables with non-unique values will be returned, otherwise all available variables will be returned

Examples


cdm <- mockIncidencePrevalence(sampleSize = 1000)
cdm <- generateDenominatorCohortSet(
  cdm = cdm, name = "denominator",
  cohortDateRange = c(as.Date("2014-01-01"), as.Date("2018-01-01"))
)
prev <- estimatePointPrevalence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome"
)
availablePrevalenceGrouping(prev)

Run benchmark of incidence and prevalence analyses

Description

Run benchmark of incidence and prevalence analyses

Usage

benchmarkIncidencePrevalence(cdm, analysisType = "all")

Arguments

cdm

A CDM reference object

analysisType

A string of the following: "all", "only incidence", "only prevalence"

Value

a tibble with time taken for different analyses

Examples


cdm <- mockIncidencePrevalence(
  sampleSize = 100,
  earliestObservationStartDate = as.Date("2010-01-01"),
  latestObservationStartDate = as.Date("2010-01-01"),
  minDaysToObservationEnd = 364,
  maxDaysToObservationEnd = 364,
  outPre = 0.1
)

timings <- benchmarkIncidencePrevalence(cdm)

Collect population incidence estimates

Description

Collect population incidence estimates

Usage

estimateIncidence(
  cdm,
  denominatorTable,
  outcomeTable,
  censorTable = NULL,
  denominatorCohortId = NULL,
  outcomeCohortId = NULL,
  censorCohortId = NULL,
  interval = "years",
  completeDatabaseIntervals = TRUE,
  outcomeWashout = Inf,
  repeatedEvents = FALSE,
  strata = list(),
  includeOverallStrata = TRUE
)

Arguments

cdm

A CDM reference object

denominatorTable

A cohort table with a set of denominator cohorts (for example, created using the generateDenominatorCohortSet() function).

outcomeTable

A cohort table in the cdm reference containing a set of outcome cohorts.

censorTable

A cohort table in the cdm reference containing a cohort to be used for censoring. Individuals will stop contributing time at risk from the date of their first record in the censor cohort. If they appear in the censor cohort before entering the denominator cohort they will be excluded. The censor cohort can only contain one record per individual.

denominatorCohortId

The cohort definition ids or the cohort names of the denominator cohorts of interest. If NULL all cohorts will be considered in the analysis.

outcomeCohortId

The cohort definition ids or the cohort names of the outcome cohorts of interest. If NULL all cohorts will be considered in the analysis.

censorCohortId

The cohort definition id or the cohort name of the cohort to be used for censoring. Must be specified if there are multiple cohorts in the censor table.

interval

Time intervals over which incidence is estimated. Can be "weeks", "months", "quarters", "years", or "overall". ISO weeks will be used for weeks. Calendar months, quarters, or years can be used, or an overall estimate for the entire time period observed (from earliest cohort start to last cohort end) can also be estimated. If more than one option is chosen then results will be estimated for each chosen interval.

completeDatabaseIntervals

TRUE/ FALSE. Where TRUE, incidence will only be estimated for those intervals where the denominator cohort captures all the interval.

outcomeWashout

The number of days used for a 'washout' period between the end of one outcome and an individual starting to contribute time at risk. If Inf, no time can be contributed after an event has occurred.

repeatedEvents

TRUE/ FALSE. If TRUE, an individual will be able to contribute multiple events during the study period (time while they are present in an outcome cohort and any subsequent washout will be excluded). If FALSE, an individual will only contribute time up to their first event.

strata

Variables added to the denominator cohort table for which to stratify estimates.

includeOverallStrata

Whether to include an overall result as well as strata specific results (when strata has been specified).

Value

Incidence estimates

Examples


cdm <- mockIncidencePrevalence(sampleSize = 1000)
cdm <- generateDenominatorCohortSet(
  cdm = cdm, name = "denominator",
  cohortDateRange = c(as.Date("2008-01-01"), as.Date("2018-01-01"))
)
inc <- estimateIncidence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome"
)

Estimate period prevalence

Description

Estimate period prevalence

Usage

estimatePeriodPrevalence(
  cdm,
  denominatorTable,
  outcomeTable,
  denominatorCohortId = NULL,
  outcomeCohortId = NULL,
  interval = "years",
  completeDatabaseIntervals = TRUE,
  fullContribution = FALSE,
  strata = list(),
  includeOverallStrata = TRUE
)

Arguments

cdm

A CDM reference object

denominatorTable

A cohort table with a set of denominator cohorts (for example, created using the generateDenominatorCohortSet() function).

outcomeTable

A cohort table in the cdm reference containing a set of outcome cohorts.

denominatorCohortId

The cohort definition ids or the cohort names of the denominator cohorts of interest. If NULL all cohorts will be considered in the analysis.

outcomeCohortId

The cohort definition ids or the cohort names of the outcome cohorts of interest. If NULL all cohorts will be considered in the analysis.

interval

Time intervals over which period prevalence is estimated. This can be "weeks", "months", "quarters", "years", or "overall". ISO weeks will be used for weeks. Calendar months, quarters, or years can be used as the period. If more than one option is chosen then results will be estimated for each chosen interval.

completeDatabaseIntervals

TRUE/ FALSE. Where TRUE, prevalence will only be estimated for those intervals where the database captures all the interval (based on the earliest and latest observation period start dates, respectively).

fullContribution

TRUE/ FALSE. Where TRUE, individuals will only be included if they in the database for the entire interval of interest. If FALSE they are only required to present for one day of the interval in order to contribute.

strata

Variables added to the denominator cohort table for which to stratify estimates.

includeOverallStrata

Whether to include an overall result as well as strata specific results (when strata has been specified).

Value

Period prevalence estimates

Examples


cdm <- mockIncidencePrevalence(sampleSize = 1000)
cdm <- generateDenominatorCohortSet(
  cdm = cdm, name = "denominator",
  cohortDateRange = c(as.Date("2008-01-01"), as.Date("2018-01-01"))
)
estimatePeriodPrevalence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome",
  interval = "months"
)

Estimate point prevalence

Description

Estimate point prevalence

Usage

estimatePointPrevalence(
  cdm,
  denominatorTable,
  outcomeTable,
  denominatorCohortId = NULL,
  outcomeCohortId = NULL,
  interval = "years",
  timePoint = "start",
  strata = list(),
  includeOverallStrata = TRUE
)

Arguments

cdm

A CDM reference object

denominatorTable

A cohort table with a set of denominator cohorts (for example, created using the generateDenominatorCohortSet() function).

outcomeTable

A cohort table in the cdm reference containing a set of outcome cohorts.

denominatorCohortId

The cohort definition ids or the cohort names of the denominator cohorts of interest. If NULL all cohorts will be considered in the analysis.

outcomeCohortId

The cohort definition ids or the cohort names of the outcome cohorts of interest. If NULL all cohorts will be considered in the analysis.

interval

Time intervals over which period prevalence is estimated. Can be "weeks", "months", "quarters", or "years". ISO weeks will be used for weeks. Calendar months, quarters, or years can be used as the period. If more than one option is chosen then results will be estimated for each chosen interval.

timePoint

where to compute the point prevalence

strata

Variables added to the denominator cohort table for which to stratify estimates.

includeOverallStrata

Whether to include an overall result as well as strata specific results (when strata has been specified).

Value

Point prevalence estimates

Examples


cdm <- mockIncidencePrevalence(sampleSize = 1000)
cdm <- generateDenominatorCohortSet(
  cdm = cdm, name = "denominator",
  cohortDateRange = c(as.Date("2008-01-01"), as.Date("2018-01-01"))
)
estimatePointPrevalence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome",
  interval = "months"
)

Identify a set of denominator populations

Description

generateDenominatorCohortSet() creates a set of cohorts that can be used for the denominator population in analyses of incidence, using estimateIncidence(), or prevalence, using estimatePointPrevalence() or estimatePeriodPrevalence().

Usage

generateDenominatorCohortSet(
  cdm,
  name,
  cohortDateRange = as.Date(c(NA, NA)),
  ageGroup = list(c(0, 150)),
  sex = "Both",
  daysPriorObservation = 0,
  requirementInteractions = TRUE
)

Arguments

cdm

A CDM reference object

name

Name of the cohort table to be created. Note if a table already exists with this name in the database (give the prefix being used for the cdm reference) it will be overwritten.

cohortDateRange

Two dates. The first indicating the earliest cohort start date and the second indicating the latest possible cohort end date. If NULL or the first date is set as missing, the earliest observation_start_date in the observation_period table will be used for the former. If NULL or the second date is set as missing, the latest observation_end_date in the observation_period table will be used for the latter.

ageGroup

A list of age groups for which cohorts will be generated. A value of list(c(0,17), c(18,30)) would, for example, lead to the creation of cohorts for those aged from 0 to 17, and from 18 to 30. In this example an individual turning 18 during the time period would appear in both cohorts (leaving the first cohort the day before their 18th birthday and entering the second from the day of their 18th birthday).

sex

Sex of the cohorts. This can be one or more of: "Male", "Female", or "Both".

daysPriorObservation

The number of days of prior observation observed in the database required for an individual to start contributing time in a cohort.

requirementInteractions

If TRUE, cohorts will be created for all combinations of ageGroup, sex, and daysPriorObservation. If FALSE, only the first value specified for the other factors will be used. Consequently, order of values matters when requirementInteractions is FALSE.

Value

A cdm reference

Examples


cdm <- mockIncidencePrevalence(sampleSize = 1000)
cdm <- generateDenominatorCohortSet(
  cdm = cdm,
  name = "denominator",
  cohortDateRange = as.Date(c("2008-01-01", "2020-01-01"))
)
cdm

Identify a set of denominator populations using a target cohort

Description

generateTargetDenominatorCohortSet() creates a set of cohorts that can be used for the denominator population in analyses of incidence, using estimateIncidence(), or prevalence, using estimatePointPrevalence() or estimatePeriodPrevalence().

Usage

generateTargetDenominatorCohortSet(
  cdm,
  name,
  targetCohortTable,
  targetCohortId = NULL,
  cohortDateRange = as.Date(c(NA, NA)),
  timeAtRisk = c(0, Inf),
  ageGroup = list(c(0, 150)),
  sex = "Both",
  daysPriorObservation = 0,
  requirementsAtEntry = TRUE,
  requirementInteractions = TRUE
)

Arguments

cdm

A CDM reference object

name

Name of the cohort table to be created.

targetCohortTable

A cohort table in the cdm reference to use to limit cohort entry and exit (with individuals only contributing to a cohort when they are contributing to the cohort in the target table).

targetCohortId

The cohort definition ids or the cohort names of the cohorts of interest for the target table. If NULL all cohorts will be considered in the analysis.

cohortDateRange

timeAtRisk

Lower and upper bound for the time at risk window to apply relative to the target cohort entry. A value of list(c(0, 30), c(31, 60)) would, for example, create one set of denominator cohorts with time up to the 30 days following target cohort entry and another set with time from 31 days following entry to 60 days. If time at risk start is after target cohort exit and/ or observation period end then no time will be contributed. If time at risk end is after cohort exit and/ or observation period, then only time up to these will be contributed.

ageGroup

A list of age groups for which cohorts will be generated. A value of list(c(0,17), c(18,30)) would, for example, lead to the creation of cohorts for those aged from 0 to 17, and from 18 to 30.

sex

Sex of the cohorts. This can be one or more of: "Male", "Female", or "Both".

daysPriorObservation

The number of days of prior observation observed in the database required for an individual to start contributing time in a cohort.

requirementsAtEntry

If TRUE, individuals must satisfy requirements for inclusion on their cohort start date for the target cohort. If FALSE, individuals will be included once they satisfy all requirements.

requirementInteractions

Value

A cdm reference

Examples


cdm <- mockIncidencePrevalence(sampleSize = 1000)
cdm <- generateTargetDenominatorCohortSet(
  cdm = cdm,
  name = "denominator",
  targetCohortTable = "target",
  cohortDateRange = as.Date(c("2008-01-01", "2020-01-01"))
)
cdm

Generate example subset of the OMOP CDM for estimating incidence and prevalence

Description

Generate example subset of the OMOP CDM for estimating incidence and prevalence

Usage

mockIncidencePrevalence(
  personTable = NULL,
  observationPeriodTable = NULL,
  targetCohortTable = NULL,
  outcomeTable = NULL,
  censorTable = NULL,
  sampleSize = 1,
  outPre = 1,
  seed = 444,
  earliestDateOfBirth = NULL,
  latestDateOfBirth = NULL,
  earliestObservationStartDate = as.Date("1900-01-01"),
  latestObservationStartDate = as.Date("2010-01-01"),
  minDaysToObservationEnd = 1,
  maxDaysToObservationEnd = 4380,
  minOutcomeDays = 1,
  maxOutcomeDays = 10,
  maxOutcomes = 1
)

Arguments

personTable

A tibble in the format of the person table.

observationPeriodTable

A tibble in the format of the observation period table.

targetCohortTable

A tibble in the format of a cohort table which can be used for stratification

outcomeTable

A tibble in the format of a cohort table which can be used for outcomes

censorTable

A tibble in the format of a cohort table which can be used for censoring

sampleSize

The number of unique patients.

outPre

The fraction of patients with an event.

seed

The seed for simulating the data set. Use the same seed to get same data set.

earliestDateOfBirth

The earliest date of birth of a patient in person table.

latestDateOfBirth

The latest date of birth of a patient in person table.

earliestObservationStartDate

The earliest observation start date for patient format.

latestObservationStartDate

The latest observation start date for patient format.

minDaysToObservationEnd

The minimum number of days of the observational integer.

maxDaysToObservationEnd

The maximum number of days of the observation period integer.

minOutcomeDays

The minimum number of days of the outcome period default set to 1.

maxOutcomeDays

The maximum number of days of the outcome period default set to 10.

maxOutcomes

The maximum possible number of outcomes per person can have default set to 1.

Value

A cdm reference to a duckdb database with mock data.

Examples


cdm <- mockIncidencePrevalence(sampleSize = 100)
cdm

Additional arguments for the functions tableIncidence.

Description

It provides a list of allowed inputs for .option argument in tableIncidence, and their given default values.

Usage

optionsTableIncidence()

Value

The default .options named list.

Examples

{
  optionsTableIncidence()
}

Additional arguments for the functions tablePrevalence.

Description

It provides a list of allowed inputs for .option argument in tablePrevalence, and their given default values.

Usage

optionsTablePrevalence()

Value

The default .options named list.

Examples

{
  optionsTablePrevalence()
}

Plot incidence results

Description

Plot incidence results

Usage

plotIncidence(
  result,
  x = "incidence_start_date",
  y = "incidence_100000_pys",
  line = FALSE,
  point = TRUE,
  ribbon = FALSE,
  ymin = "incidence_100000_pys_95CI_lower",
  ymax = "incidence_100000_pys_95CI_upper",
  facet = NULL,
  colour = NULL
)

Arguments

result

Incidence results

x

Variable to plot on x axis

y

Variable to plot on y axis.

line

Whether to plot a line using geom_line

point

Whether to plot points using geom_point

ribbon

Whether to plot a ribbon using geom_ribbon

ymin

Lower limit of error bars, if provided is plot using geom_errorbar

ymax

Upper limit of error bars, if provided is plot using geom_errorbar

facet

Variables to use for facets. To see available variables for facetting use the function availableIncidenceGrouping().

colour

Variables to use for colours. To see available variables for colouring use the function availableIncidenceGrouping().

Value

A ggplot with the incidence results plotted

Examples


cdm <- mockIncidencePrevalence(sampleSize = 1000)
cdm <- generateDenominatorCohortSet(
  cdm = cdm, name = "denominator",
  cohortDateRange = c(as.Date("2008-01-01"), as.Date("2018-01-01"))
)
inc <- estimateIncidence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome"
)
plotIncidence(inc)

Bar plot of denominator counts, outcome counts, and person-time from incidence results

Description

Bar plot of denominator counts, outcome counts, and person-time from incidence results

Usage

plotIncidencePopulation(
  result,
  x = "incidence_start_date",
  y = "denominator_count",
  facet = NULL,
  colour = NULL
)

Arguments

result

Incidence results

x

Variable to plot on x axis

y

Variable to plot on y axis.

facet

Variables to use for facets. To see available variables for facetting use the functions availableIncidenceGrouping().

colour

Variables to use for colours. To see available variables for colouring use the function availableIncidenceGrouping().

Value

A ggplot object

Examples


cdm <- mockIncidencePrevalence(sampleSize = 1000)
cdm <- generateDenominatorCohortSet(
  cdm = cdm, name = "denominator",
  cohortDateRange = c(as.Date("2014-01-01"), as.Date("2018-01-01"))
)
inc <- estimateIncidence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome"
)
plotIncidencePopulation(inc)

Plot prevalence results

Description

Plot prevalence results

Usage

plotPrevalence(
  result,
  x = "prevalence_start_date",
  y = "prevalence",
  line = FALSE,
  point = TRUE,
  ribbon = FALSE,
  ymin = "prevalence_95CI_lower",
  ymax = "prevalence_95CI_upper",
  facet = NULL,
  colour = NULL
)

Arguments

result

Prevalence results

x

Variable to plot on x axis

y

Variable to plot on y axis.

line

Whether to plot a line using geom_line

point

Whether to plot points using geom_point

ribbon

Whether to plot a ribbon using geom_ribbon

ymin

Lower limit of error bars, if provided is plot using geom_errorbar

ymax

Upper limit of error bars, if provided is plot using geom_errorbar

facet

Variables to use for facets. To see available variables for facetting use the function availablePrevalenceGrouping().

colour

Variables to use for colours. To see available variables for colouring use the function availablePrevalenceGrouping().

Value

A ggplot with the prevalence results plotted

Examples


cdm <- mockIncidencePrevalence(sampleSize = 1000)
cdm <- generateDenominatorCohortSet(
  cdm = cdm, name = "denominator",
  cohortDateRange = c(as.Date("2014-01-01"), as.Date("2018-01-01"))
)
prev <- estimatePointPrevalence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome"
)
plotPrevalence(prev)

Bar plot of denominator and outcome counts from prevalence results

Description

Bar plot of denominator and outcome counts from prevalence results

Usage

plotPrevalencePopulation(
  result,
  x = "prevalence_start_date",
  y = "denominator_count",
  facet = NULL,
  colour = NULL
)

Arguments

result

Prevalence results

x

Variable to plot on x axis

y

Variable to plot on y axis.

facet

Variables to use for facets. To see available variables for facetting use the functions availablePrevalenceGrouping().

colour

Variables to use for colours. To see available variables for colouring use the function availablePrevalenceGrouping().

Value

A ggplot object

Examples


cdm <- mockIncidencePrevalence(sampleSize = 1000)
cdm <- generateDenominatorCohortSet(
  cdm = cdm, name = "denominator",
  cohortDateRange = c(as.Date("2014-01-01"), as.Date("2018-01-01"))
)
prev <- estimatePointPrevalence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome"
)
plotPrevalencePopulation(prev)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

omopgenerics: attrition, bind, cohortCodelist, cohortCount, exportSummarisedResult, importSummarisedResult, settings, suppress

Table of incidence results

Description

Table of incidence results

Usage

tableIncidence(
  result,
  type = "gt",
  header = c("estimate_name"),
  groupColumn = c("cdm_name", "outcome_cohort_name"),
  settingsColumn = c("denominator_age_group", "denominator_sex"),
  hide = c("denominator_cohort_name", "analysis_interval"),
  .options = list()
)

Arguments

result

Incidence results

type

Type of table. Can be "gt", "flextable", or "tibble"

header

A vector specifying the elements to include in the header. The order of elements matters, with the first being the topmost header. The header vector can contain one of the following variables: "cdm_name", "denominator_cohort_name", "outcome_cohort_name", "incidence_start_date", "incidence_end_date", "estimate_name", variables in the strata_name column, and any of the settings columns specified in settingsColumn argument. The header can also include other names to use as overall header labels

groupColumn

Variables to use as group labels. Allowed columns are the same as in header

settingsColumn

Variables from the settings attribute to display in the table

hide

Table columns to exclude, options are the ones described in header

.options

Table options to apply

Value

Table of results

Examples


cdm <- mockIncidencePrevalence(sampleSize = 1000)
cdm <- generateDenominatorCohortSet(
  cdm = cdm, name = "denominator",
  cohortDateRange = c(as.Date("2008-01-01"), as.Date("2018-01-01"))
)
inc <- estimateIncidence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome"
)
tableIncidence(inc)

Table of incidence attrition results

Description

Table of incidence attrition results

Usage

tableIncidenceAttrition(
  result,
  type = "gt",
  header = "variable_name",
  groupColumn = c("cdm_name", "outcome_cohort_name"),
  settingsColumn = NULL,
  hide = c("denominator_cohort_name", "estimate_name", "reason_id", "variable_level")
)

Arguments

result

A summarised_result object. Output of summariseCohortAttrition().

type

Type of table. Check supported types with visOmopResults::tableType().

header

Columns to use as header. See options with colnames(omopgenerics::splitAll(result)). Variables in settingsColumn are also allowed

groupColumn

Variables to use as group labels. Allowed columns are the same as in header

settingsColumn

Variables from the settings attribute to display in the table

hide

Table columns to exclude, options are the ones described in header

Value

A visual table.

Examples


cdm <- mockIncidencePrevalence(sampleSize = 1000)
cdm <- generateDenominatorCohortSet(
  cdm = cdm, name = "denominator",
  cohortDateRange = c(as.Date("2008-01-01"), as.Date("2018-01-01"))
)
inc <- estimateIncidence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome"
)
tableIncidenceAttrition(inc)

Table of prevalence results

Description

Table of prevalence results

Usage

tablePrevalence(
  result,
  type = "gt",
  header = c("estimate_name"),
  groupColumn = c("cdm_name", "outcome_cohort_name"),
  settingsColumn = c("denominator_age_group", "denominator_sex"),
  hide = c("denominator_cohort_name", "analysis_interval"),
  .options = list()
)

Arguments

result

Prevalence results

type

Type of table. Can be "gt", "flextable", or "tibble"

header

A vector specifying the elements to include in the header. The order of elements matters, with the first being the topmost header. The header vector can contain one of the following variables: "cdm_name", "denominator_cohort_name", "outcome_cohort_name", "prevalence_start_date", "prevalence_end_date", "estimate_name", variables in the strata_name column, and any of the settings columns specified in settingsColumn argument. The header can also include other names to use as overall header labels

groupColumn

Variables to use as group labels. Allowed columns are the same as in header

settingsColumn

Variables from the settings attribute to display in the table

hide

Table columns to exclude, options are the ones described in header

.options

Table options to apply

Value

Table of prevalence results

Examples


cdm <- mockIncidencePrevalence(sampleSize = 1000)
cdm <- generateDenominatorCohortSet(
  cdm = cdm,
  name = "denominator",
  cohortDateRange = c(as.Date("2008-01-01"), as.Date("2018-01-01"))
)
prev <- estimatePointPrevalence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome",
  interval = "months"
)
tablePrevalence(prev)

Table of prevalence attrition results

Description

Table of prevalence attrition results

Usage

tablePrevalenceAttrition(
  result,
  type = "gt",
  header = "variable_name",
  groupColumn = c("cdm_name", "outcome_cohort_name"),
  settingsColumn = NULL,
  hide = c("denominator_cohort_name", "estimate_name", "reason_id", "variable_level")
)

Arguments

result

A summarised_result object. Output of summariseCohortAttrition().

type

Type of table. Check supported types with visOmopResults::tableType().

header

Columns to use as header. See options with colnames(omopgenerics::splitAll(result)). Variables in settingsColumn are also allowed

groupColumn

Variables to use as group labels. Allowed columns are the same as in header

settingsColumn

Variables from the settings attribute to display in the table

hide

Table columns to exclude, options are the ones described in header

Value

A visual table.

Examples


cdm <- mockIncidencePrevalence(sampleSize = 1000)
cdm <- generateDenominatorCohortSet(
  cdm = cdm, name = "denominator",
  cohortDateRange = c(as.Date("2008-01-01"), as.Date("2018-01-01"))
)
prev <- estimatePointPrevalence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome",
  interval = "months"
)
tablePrevalenceAttrition(prev)

IncidencePrevalence: Estimate Incidence and Prevalence using the OMOP Common Data Model

Description

Author(s)

See Also

Pipe operator

Description

Usage

Arguments

Value

Benchmarking results

Description

Usage

Format

A tidy implementation of the summarised_result object for incidence results.

Description

Usage

Arguments

Value

Examples

A tidy implementation of the summarised_result object for prevalence results.

Description

Usage

Arguments

Value

Examples

Variables that can be used for faceting and colouring incidence plots

Description

Usage

Arguments

Examples

Variables that can be used for faceting and colouring prevalence plots

Description

Usage

Arguments

Examples

Run benchmark of incidence and prevalence analyses

Description

Usage

Arguments

Value

Examples

Collect population incidence estimates

Description

Usage

Arguments

Value

Examples

Estimate period prevalence

Description

Usage

Arguments

Value

Examples

Estimate point prevalence

Description

Usage

Arguments

Value

Examples

Identify a set of denominator populations

Description

Usage

Arguments

Value

Examples

Identify a set of denominator populations using a target cohort

Description

Usage

Arguments

Value

Examples

Generate example subset of the OMOP CDM for estimating incidence and prevalence

Description

Usage

Arguments

Value

Examples

Additional arguments for the functions tableIncidence.

Description

Usage