Package 'IncidencePrevalence' reference manual

Title:	Estimate Incidence and Prevalence using the OMOP Common Data Model
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.
Authors:	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 <[email protected]>
License:	Apache License (>= 2)
Version:	0.7.4
Built:	2024-09-30 06:39:54 UTC
Source:	CRAN

Run benchmark of incidence and prevalence analyses

Description

Run benchmark of incidence and prevalence analyses

Usage

benchmarkIncidencePrevalence(
  cdm,
  returnParticipants = FALSE,
  analysisType = "all"
)
benchmarkIncidencePrevalence(
  cdm,
  returnParticipants = FALSE,
  analysisType = "all"
)

Arguments

`cdm`	A CDM reference object
`returnParticipants`	Whether to return participants
`analysisType`	A string of the following: "all", "only incidence", "only prevalence"

Value

a tibble with time taken for different analyses

Examples


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

timings <- benchmarkIncidencePrevalence(cdm)

cdm <- mockIncidencePrevalenceRef(
  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,
  denominatorCohortId = NULL,
  outcomeCohortId = NULL,
  interval = "years",
  completeDatabaseIntervals = TRUE,
  outcomeWashout = Inf,
  repeatedEvents = FALSE,
  minCellCount = 5,
  strata = list(),
  includeOverallStrata = TRUE,
  returnParticipants = FALSE
)
estimateIncidence(
  cdm,
  denominatorTable,
  outcomeTable,
  denominatorCohortId = NULL,
  outcomeCohortId = NULL,
  interval = "years",
  completeDatabaseIntervals = TRUE,
  outcomeWashout = Inf,
  repeatedEvents = FALSE,
  minCellCount = 5,
  strata = list(),
  includeOverallStrata = TRUE,
  returnParticipants = FALSE
)

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 of the denominator cohorts of interest. If NULL all cohorts will be considered in the analysis.
`outcomeCohortId`	The cohort definition ids of the outcome cohorts of interest. If NULL all cohorts will be considered in the analysis.
`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.
`minCellCount`	The minimum number of events to reported, below which results will be obscured. If 0, all results will be reported.
`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).
`returnParticipants`	Either TRUE or FALSE. If TRUE references to participants from the analysis will be returned allowing for further analysis. Note, if using permanent tables and returnParticipants is TRUE, one table per analysis will be kept in the cdm write schema.

Value

Incidence estimates

Examples


cdm <- mockIncidencePrevalenceRef(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"
)

cdm <- mockIncidencePrevalenceRef(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,
  minCellCount = 5,
  returnParticipants = FALSE
)
estimatePeriodPrevalence(
  cdm,
  denominatorTable,
  outcomeTable,
  denominatorCohortId = NULL,
  outcomeCohortId = NULL,
  interval = "years",
  completeDatabaseIntervals = TRUE,
  fullContribution = FALSE,
  strata = list(),
  includeOverallStrata = TRUE,
  minCellCount = 5,
  returnParticipants = FALSE
)

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 of the denominator cohorts of interest. If NULL all cohorts will be considered in the analysis.
`outcomeCohortId`	The cohort definition ids 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).
`minCellCount`	Minimum number of events to report- results lower than this will be obscured. If NULL all results will be reported.
`returnParticipants`	Either TRUE or FALSE. If TRUE references to participants from the analysis will be returned allowing for further analysis. Note, if using permanent tables and returnParticipants is TRUE, one table per analysis will be kept in the cdm write schema.

Value

Period prevalence estimates

Examples


cdm <- mockIncidencePrevalenceRef(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"
)

cdm <- mockIncidencePrevalenceRef(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,
  minCellCount = 5,
  returnParticipants = FALSE
)
estimatePointPrevalence(
  cdm,
  denominatorTable,
  outcomeTable,
  denominatorCohortId = NULL,
  outcomeCohortId = NULL,
  interval = "years",
  timePoint = "start",
  strata = list(),
  includeOverallStrata = TRUE,
  minCellCount = 5,
  returnParticipants = FALSE
)

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 of the denominator cohorts of interest. If NULL all cohorts will be considered in the analysis.
`outcomeCohortId`	The cohort definition ids 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).
`minCellCount`	Minimum number of events to report- results lower than this will be obscured. If NULL all results will be reported.
`returnParticipants`	Either TRUE or FALSE. If TRUE references to participants from the analysis will be returned allowing for further analysis. Note, if using permanent tables and returnParticipants is TRUE, one table per analysis will be kept in the cdm write schema.

Value

Point prevalence estimates

Examples


cdm <- mockIncidencePrevalenceRef(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"
)

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

cdm <- mockIncidencePrevalenceRef(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)),
  ageGroup = list(c(0, 150)),
  sex = "Both",
  daysPriorObservation = 0,
  requirementInteractions = TRUE
)
generateTargetDenominatorCohortSet(
  cdm,
  name,
  targetCohortTable,
  targetCohortId = NULL,
  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.
`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 id for the cohort of interest in the target table. If targetCohortTable is specified, a single targetCohortId must also be specified.
`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 <- mockIncidencePrevalenceRef(sampleSize = 1000)
cdm <- generateTargetDenominatorCohortSet(
  cdm = cdm,
  name = "denominator",
  targetCohortTable = "target",
  cohortDateRange = as.Date(c("2008-01-01", "2020-01-01"))
)
cdm

cdm <- mockIncidencePrevalenceRef(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

mockIncidencePrevalenceRef(
  personTable = NULL,
  observationPeriodTable = NULL,
  targetCohortTable = NULL,
  outcomeTable = NULL,
  sampleSize = 1,
  outPre = 1,
  seed = 444,
  ageBeta = NULL,
  genderBeta = NULL,
  intercept = NULL,
  earliestDateOfBirth = NULL,
  latestDateOfBirth = NULL,
  earliestObservationStartDate = NULL,
  latestObservationStartDate = NULL,
  minDaysToObservationEnd = NULL,
  maxDaysToObservationEnd = NULL,
  minOutcomeDays = 1,
  maxOutcomeDays = 10,
  maxOutcomes = 1
)
mockIncidencePrevalenceRef(
  personTable = NULL,
  observationPeriodTable = NULL,
  targetCohortTable = NULL,
  outcomeTable = NULL,
  sampleSize = 1,
  outPre = 1,
  seed = 444,
  ageBeta = NULL,
  genderBeta = NULL,
  intercept = NULL,
  earliestDateOfBirth = NULL,
  latestDateOfBirth = NULL,
  earliestObservationStartDate = NULL,
  latestObservationStartDate = NULL,
  minDaysToObservationEnd = NULL,
  maxDaysToObservationEnd = NULL,
  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
`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.
`ageBeta`	The beta for the standardised age in a logistic regression outcome model.
`genderBeta`	The beta for the gender flag in a logistic regression outcome model.
`intercept`	The beta for the intercept in a logistic regression outcome model.
`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 <- mockIncidencePrevalenceRef(sampleSize = 100)
cdm


cdm <- mockIncidencePrevalenceRef(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()
optionsTableIncidence()

Value

The default .options named list.

Examples

{
optionsTableIncidence()
}

{
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()
optionsTablePrevalence()

Value

The default .options named list.

Examples

{
optionsTablePrevalence()
}

{
optionsTablePrevalence()
}

Participants contributing to an analysis

Description

Participants contributing to an analysis

Usage

participants(result, analysisId)
participants(result, analysisId)

Arguments

`result`	Result object
`analysisId`	ID of a specific analysis to return participants for

Value

References to tables with the study participants contributing to a given analysis

Examples


cdm <- mockIncidencePrevalenceRef(sampleSize = 200)
cdm <- generateDenominatorCohortSet(cdm, name = "denominator")
incidence <- estimateIncidence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome",
  interval = "overall"
)
participants(result = incidence, analysisId = 1)

cdm <- mockIncidencePrevalenceRef(sampleSize = 200)
cdm <- generateDenominatorCohortSet(cdm, name = "denominator")
incidence <- estimateIncidence(
  cdm = cdm,
  denominatorTable = "denominator",
  outcomeTable = "outcome",
  interval = "overall"
)
participants(result = incidence, analysisId = 1)

Plot incidence results

Description

Plot incidence results

Usage

plotIncidence(
  result,
  x = "incidence_start_date",
  ylim = c(0, NA),
  ribbon = FALSE,
  facet = NULL,
  colour = NULL,
  colour_name = NULL,
  options = list()
)
plotIncidence(
  result,
  x = "incidence_start_date",
  ylim = c(0, NA),
  ribbon = FALSE,
  facet = NULL,
  colour = NULL,
  colour_name = NULL,
  options = list()
)

Arguments

`result`	Incidence results
`x`	Variable to plot on x axis
`ylim`	Limits for the Y axis
`ribbon`	If TRUE, the plot will join points using a ribbon
`facet`	Variables to use for facets
`colour`	Variables to use for colours
`colour_name`	Colour legend name
`options`	a list of optional plot options

Value

A ggplot with the incidence results plotted

Examples


cdm <- mockIncidencePrevalenceRef(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)

cdm <- mockIncidencePrevalenceRef(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)

Plot prevalence results

Description

Plot prevalence results

Usage

plotPrevalence(
  result,
  x = "prevalence_start_date",
  ylim = c(0, NA),
  ribbon = FALSE,
  facet = NULL,
  colour = NULL,
  colour_name = NULL,
  options = list()
)
plotPrevalence(
  result,
  x = "prevalence_start_date",
  ylim = c(0, NA),
  ribbon = FALSE,
  facet = NULL,
  colour = NULL,
  colour_name = NULL,
  options = list()
)

Arguments

`result`	Prevalence results
`x`	Variable to plot on x axis
`ylim`	Limits for the Y axis
`ribbon`	If TRUE, the plot will join points using a ribbon
`facet`	Variables to use for facets
`colour`	Variables to use for colours
`colour_name`	Colour legend name
`options`	a list of optional plot options

Value

A ggplot with the prevalence results plotted

Examples


cdm <- mockIncidencePrevalenceRef(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)

cdm <- mockIncidencePrevalenceRef(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)

Package 'IncidencePrevalence'

Help Index

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

Value

Examples

Additional arguments for the functions tablePrevalence.

Description

Usage

Value

Examples

Participants contributing to an analysis

Description

Usage

Arguments

Value

Examples

Plot incidence results

Description

Usage

Arguments

Value

Examples

Plot prevalence results

Description

Usage

Arguments

Value

Examples