Package 'TreatmentPatterns' reference manual

Title:	Analyzes Real-World Treatment Patterns of a Study Population of Interest
Description:	Computes treatment patterns within a given cohort using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). As described in Markus, Verhamme, Kors, and Rijnbeek (2022) <doi:10.1016/j.cmpb.2022.107081>.
Authors:	Aniek Markus [aut] , Maarten van Kessel [cre]
Maintainer:	Maarten van Kessel <[email protected]>
License:	Apache License 2.0
Version:	2.6.9
Built:	2024-10-31 15:20:26 UTC
Source:	https://github.com/darwin-eu/treatmentpatterns

CharacterizationPlots

Description

Class to handle the characterization plots.

Super class

TreatmentPatterns::ShinyModule -> CharacterizationPlots

Methods

Inherited methods

TreatmentPatterns::ShinyModule$initialize()
TreatmentPatterns::ShinyModule$validate()

Method `uiMenu()`

Method to include a menuItem to link to the body.

Usage

CharacterizationPlots$uiMenu(
  label = "Characteristics",
  tag = "characteristics"
)

Arguments

label: (character(1))
Label to show for the menuItem.
tag: (character(1))
Tag to use internally in input.

Returns

(menuItem)

Method `uiBody()`

Method to include a tabItem to include the body.

Usage

CharacterizationPlots$uiBody()

Returns

(tabItem)

Method `server()`

Method to handle the back-end.

Usage

CharacterizationPlots$server(input, output, session, inputHandler)

Arguments

input: (input)
Input from the server function.
output: (output)
Output from the server function.
session: (session)
Session from the server function.
inputHandler: (inputHandler)
InputHandler class.

Returns

(NULL)

Method `clone()`

The objects of this class are cloneable with this method.

Usage

CharacterizationPlots$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

computePathways

Description

Compute treatment patterns according to the specified parameters within specified cohorts.

Usage

computePathways(
  cohorts,
  cohortTableName,
  cdm = NULL,
  connectionDetails = NULL,
  cdmSchema = NULL,
  resultSchema = NULL,
  tempEmulationSchema = NULL,
  includeTreatments = "startDate",
  periodPriorToIndex = 0,
  minEraDuration = 0,
  splitEventCohorts = NULL,
  splitTime = NULL,
  eraCollapseSize = 30,
  combinationWindow = 30,
  minPostCombinationDuration = 30,
  filterTreatments = "First",
  maxPathLength = 5
)
computePathways(
  cohorts,
  cohortTableName,
  cdm = NULL,
  connectionDetails = NULL,
  cdmSchema = NULL,
  resultSchema = NULL,
  tempEmulationSchema = NULL,
  includeTreatments = "startDate",
  periodPriorToIndex = 0,
  minEraDuration = 0,
  splitEventCohorts = NULL,
  splitTime = NULL,
  eraCollapseSize = 30,
  combinationWindow = 30,
  minPostCombinationDuration = 30,
  filterTreatments = "First",
  maxPathLength = 5
)

Arguments

`cohorts`	(`data.frame()`) Data frame containing the following columns and data types: cohortId `numeric(1)` Cohort ID's of the cohorts to be used in the cohort table. cohortName `character(1)` Cohort names of the cohorts to be used in the cohort table. type `character(1)` ["target", "event', "exit"] Cohort type, describing if the cohort is a target, event, or exit cohort
`cohortTableName`	(`character(1)`) Cohort table name.
`cdm`	(`CDMConnector::cdm_from_con()`: `NULL`) Optional; Ignores `connectionDetails`, `cdmSchema`, and `resultSchema`.
`connectionDetails`	(`DatabaseConnector::createConnectionDetails()`: `NULL`) Optional; In congruence with `cdmSchema` and `resultSchema`. Ignores `cdm`.
`cdmSchema`	(`character(1)`: `NULL`) Optional; In congruence with `connectionDetails` and `resultSchema`. Ignores `cdm`.
`resultSchema`	(`character(1)`: `NULL`) Optional; In congruence with `connectionDetails` and `cdmSchema`. Ignores `cdm`.
`tempEmulationSchema`	Schema used to emulate temp tables
`includeTreatments`	(`character(1)`: `"startDate"`) `"startDate"` Include treatments after the target cohort start date and onwards. `"endDate"` Include treatments before target cohort end date and before.
`periodPriorToIndex`	(`integer(1)`: `0`) Number of days prior to the index date of the target cohort \| that event cohorts are allowed to start
`minEraDuration`	(`integer(1)`: `0`) Minimum time an event era should last to be included in analysis
`splitEventCohorts`	(`character(n)`: `""`) Specify event cohort to split in acute (< X days) and therapy (>= X days)
`splitTime`	(`integer(1)`: `30`) Specify number of days (X) at which each of the split event cohorts should be split in acute and therapy
`eraCollapseSize`	(`integer(1)`: `30`) Window of time between which two eras of the same event cohort are collapsed into one era
`combinationWindow`	(`integer(1)`: `30`) Window of time two event cohorts need to overlap to be considered a combination treatment
`minPostCombinationDuration`	(`integer(1)`: `30`) Minimum time an event era before or after a generated combination treatment should last to be included in analysis
`filterTreatments`	(`character(1)`: `"First"` ["first", "Changes", "all"]) Select first occurrence of (‘First’); changes between (‘Changes’); or all event cohorts (‘All’).
`maxPathLength`	(`integer(1)`: `5`) Maximum number of steps included in treatment pathway

Value

(Andromeda::andromeda()) andromeda object containing non-sharable patient level data outcomes.

Examples


ableToRun <- all(
  require("CirceR", character.only = TRUE, quietly = TRUE),
  require("CDMConnector", character.only = TRUE, quietly = TRUE),
  require("TreatmentPatterns", character.only = TRUE, quietly = TRUE),
  require("dplyr", character.only = TRUE, quietly = TRUE)
)

if (ableToRun) {
  library(TreatmentPatterns)
  library(CDMConnector)
  library(dplyr)

  withr::local_envvar(
    R_USER_CACHE_DIR = tempfile(),
    EUNOMIA_DATA_FOLDER = Sys.getenv("EUNOMIA_DATA_FOLDER", unset = tempfile())
  )

  tryCatch({
    if (Sys.getenv("skip_eunomia_download_test") != "TRUE") {
      CDMConnector::downloadEunomiaData(overwrite = TRUE)
    }
  }, error = function(e) NA)

  con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir())
  cdm <- cdmFromCon(con, cdmSchema = "main", writeSchema = "main")

  cohortSet <- readCohortSet(
    path = system.file(package = "TreatmentPatterns", "exampleCohorts")
  )

  cdm <- generateCohortSet(
    cdm = cdm,
    cohortSet = cohortSet,
    name = "cohort_table"
  )

  cohorts <- cohortSet %>%
    # Remove 'cohort' and 'json' columns
    select(-"cohort", -"json") %>%
    mutate(type = c("event", "event", "event", "event", "exit", "event", "event", "target")) %>%
    rename(
      cohortId = "cohort_definition_id",
      cohortName = "cohort_name",
    ) %>%
    select("cohortId", "cohortName", "type")

  outputEnv <- computePathways(
    cohorts = cohorts,
    cohortTableName = "cohort_table",
    cdm = cdm
  )

  Andromeda::close(outputEnv)
  DBI::dbDisconnect(con, shutdown = TRUE)
}

ableToRun <- all(
  require("CirceR", character.only = TRUE, quietly = TRUE),
  require("CDMConnector", character.only = TRUE, quietly = TRUE),
  require("TreatmentPatterns", character.only = TRUE, quietly = TRUE),
  require("dplyr", character.only = TRUE, quietly = TRUE)
)

if (ableToRun) {
  library(TreatmentPatterns)
  library(CDMConnector)
  library(dplyr)

  withr::local_envvar(
    R_USER_CACHE_DIR = tempfile(),
    EUNOMIA_DATA_FOLDER = Sys.getenv("EUNOMIA_DATA_FOLDER", unset = tempfile())
  )

  tryCatch({
    if (Sys.getenv("skip_eunomia_download_test") != "TRUE") {
      CDMConnector::downloadEunomiaData(overwrite = TRUE)
    }
  }, error = function(e) NA)

  con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir())
  cdm <- cdmFromCon(con, cdmSchema = "main", writeSchema = "main")

  cohortSet <- readCohortSet(
    path = system.file(package = "TreatmentPatterns", "exampleCohorts")
  )

  cdm <- generateCohortSet(
    cdm = cdm,
    cohortSet = cohortSet,
    name = "cohort_table"
  )

  cohorts <- cohortSet %>%
    # Remove 'cohort' and 'json' columns
    select(-"cohort", -"json") %>%
    mutate(type = c("event", "event", "event", "event", "exit", "event", "event", "target")) %>%
    rename(
      cohortId = "cohort_definition_id",
      cohortName = "cohort_name",
    ) %>%
    select("cohortId", "cohortName", "type")

  outputEnv <- computePathways(
    cohorts = cohorts,
    cohortTableName = "cohort_table",
    cdm = cdm
  )

  Andromeda::close(outputEnv)
  DBI::dbDisconnect(con, shutdown = TRUE)
}

createSankeyDiagram

Description

Create sankey diagram.

Usage

createSankeyDiagram(
  treatmentPathways,
  groupCombinations = FALSE,
  colors = NULL,
  ...
)
createSankeyDiagram(
  treatmentPathways,
  groupCombinations = FALSE,
  colors = NULL,
  ...
)

Arguments

`treatmentPathways`	(`data.frame()`) The contents of the treatmentPathways.csv-file as a data.frame().
`groupCombinations`	(`logical(1)`: `FALSE`) `TRUE` Group all combination treatments in category `"Combination"`. `FALSE` Do not group combination treatments.
`colors`	(`character(n)`) Vector of hex color codes.
`...`	Paramaters for sankeyNetwork.

Value

(htmlwidget)

Examples

# Dummy data, typically read from treatmentPathways.csv
treatmentPathways <- data.frame(
  path = c("Acetaminophen", "Acetaminophen-Amoxicillin+Clavulanate",
           "Acetaminophen-Aspirin", "Amoxicillin+Clavulanate", "Aspirin"),
  freq = c(206, 6, 14, 48, 221),
  sex = rep("all", 5),
  age = rep("all", 5),
  index_year = rep("all", 5)
)

createSankeyDiagram(treatmentPathways)
# Dummy data, typically read from treatmentPathways.csv
treatmentPathways <- data.frame(
  path = c("Acetaminophen", "Acetaminophen-Amoxicillin+Clavulanate",
           "Acetaminophen-Aspirin", "Amoxicillin+Clavulanate", "Aspirin"),
  freq = c(206, 6, 14, 48, 221),
  sex = rep("all", 5),
  age = rep("all", 5),
  index_year = rep("all", 5)
)

createSankeyDiagram(treatmentPathways)

createSankeyDiagram2

Description

DEPRECATED Create sankey diagram.

Usage

createSankeyDiagram2(
  treatmentPathways,
  groupCombinations = FALSE,
  colors = NULL,
  ...
)
createSankeyDiagram2(
  treatmentPathways,
  groupCombinations = FALSE,
  colors = NULL,
  ...
)

Arguments

`treatmentPathways`	(`data.frame()`) The contents of the treatmentPathways.csv-file as a data.frame().
`groupCombinations`	(`logical(1)`: `FALSE`) `TRUE` Group all combination treatments in category `"Combination"`. `FALSE` Do not group combination treatments.
`colors`	(`character(n)`) Vector of hex color codes.
`...`	Paramaters for sankeyNetwork.

Value

(htmlwidget)

Examples

# Dummy data, typically read from treatmentPathways.csv
treatmentPathways <- data.frame(
  path = c("Acetaminophen", "Acetaminophen-Amoxicillin+Clavulanate",
           "Acetaminophen-Aspirin", "Amoxicillin+Clavulanate", "Aspirin"),
  freq = c(206, 6, 14, 48, 221),
  sex = rep("all", 5),
  age = rep("all", 5),
  index_year = rep("all", 5)
)

createSankeyDiagram(treatmentPathways)
# Dummy data, typically read from treatmentPathways.csv
treatmentPathways <- data.frame(
  path = c("Acetaminophen", "Acetaminophen-Amoxicillin+Clavulanate",
           "Acetaminophen-Aspirin", "Amoxicillin+Clavulanate", "Aspirin"),
  freq = c(206, 6, 14, 48, 221),
  sex = rep("all", 5),
  age = rep("all", 5),
  index_year = rep("all", 5)
)

createSankeyDiagram(treatmentPathways)

createSunburstPlot

Description

New sunburstPlot function

Usage

createSunburstPlot(treatmentPathways, groupCombinations = FALSE, ...)
createSunburstPlot(treatmentPathways, groupCombinations = FALSE, ...)

Arguments

treatmentPathways

(data.frame())
The contents of the treatmentPathways.csv-file as a data.frame().

groupCombinations

(logical(1): FALSE)

TRUE: Group all combination treatments in category "Combination".
FALSE: Do not group combination treatments.

...

Paramaters for sunburst.

Value

(htmlwidget)

Examples

# Dummy data, typically read from treatmentPathways.csv
treatmentPatwhays <- data.frame(
  path = c("Acetaminophen", "Acetaminophen-Amoxicillin+Clavulanate",
           "Acetaminophen-Aspirin", "Amoxicillin+Clavulanate", "Aspirin"),
  freq = c(206, 6, 14, 48, 221),
  sex = rep("all", 5),
  age = rep("all", 5),
  index_year = rep("all", 5)
)

createSunburstPlot(treatmentPatwhays)
# Dummy data, typically read from treatmentPathways.csv
treatmentPatwhays <- data.frame(
  path = c("Acetaminophen", "Acetaminophen-Amoxicillin+Clavulanate",
           "Acetaminophen-Aspirin", "Amoxicillin+Clavulanate", "Aspirin"),
  freq = c(206, 6, 14, 48, 221),
  sex = rep("all", 5),
  age = rep("all", 5),
  index_year = rep("all", 5)
)

createSunburstPlot(treatmentPatwhays)

createSunburstPlot2

Description

DEPRECATED New sunburstPlot function

Usage

createSunburstPlot2(treatmentPathways, groupCombinations = FALSE, ...)
createSunburstPlot2(treatmentPathways, groupCombinations = FALSE, ...)

Arguments

treatmentPathways

(data.frame())
The contents of the treatmentPathways.csv-file as a data.frame().

groupCombinations

(logical(1): FALSE)

TRUE: Group all combination treatments in category "Combination".
FALSE: Do not group combination treatments.

...

Paramaters for sunburst.

Value

(htmlwidget)

Examples

# Dummy data, typically read from treatmentPathways.csv
treatmentPatwhays <- data.frame(
  path = c("Acetaminophen", "Acetaminophen-Amoxicillin+Clavulanate",
           "Acetaminophen-Aspirin", "Amoxicillin+Clavulanate", "Aspirin"),
  freq = c(206, 6, 14, 48, 221),
  sex = rep("all", 5),
  age = rep("all", 5),
  index_year = rep("all", 5)
)

createSunburstPlot2(treatmentPatwhays)
# Dummy data, typically read from treatmentPathways.csv
treatmentPatwhays <- data.frame(
  path = c("Acetaminophen", "Acetaminophen-Amoxicillin+Clavulanate",
           "Acetaminophen-Aspirin", "Amoxicillin+Clavulanate", "Aspirin"),
  freq = c(206, 6, 14, 48, 221),
  sex = rep("all", 5),
  age = rep("all", 5),
  index_year = rep("all", 5)
)

createSunburstPlot2(treatmentPatwhays)

Compute treatment patterns according to the specified parameters within specified cohorts. For more customization, or investigation of patient level outcomes, you can run computePathways and export separately.

Usage

executeTreatmentPatterns(
  cohorts,
  cohortTableName,
  outputPath,
  cdm = NULL,
  connectionDetails = NULL,
  cdmSchema = NULL,
  resultSchema = NULL,
  tempEmulationSchema = NULL,
  minEraDuration = 0,
  eraCollapseSize = 30,
  combinationWindow = 30,
  minCellCount = 5
)
executeTreatmentPatterns(
  cohorts,
  cohortTableName,
  outputPath,
  cdm = NULL,
  connectionDetails = NULL,
  cdmSchema = NULL,
  resultSchema = NULL,
  tempEmulationSchema = NULL,
  minEraDuration = 0,
  eraCollapseSize = 30,
  combinationWindow = 30,
  minCellCount = 5
)

Arguments

`cohorts`	(`data.frame()`) Data frame containing the following columns and data types: cohortId `numeric(1)` Cohort ID's of the cohorts to be used in the cohort table. cohortName `character(1)` Cohort names of the cohorts to be used in the cohort table. type `character(1)` ["target", "event', "exit"] Cohort type, describing if the cohort is a target, event, or exit cohort
`cohortTableName`	(`character(1)`) Cohort table name.
`outputPath`	(`character(1)`)
`cdm`	(`CDMConnector::cdm_from_con()`: `NULL`) Optional; Ignores `connectionDetails`, `cdmSchema`, and `resultSchema`.
`connectionDetails`	(`DatabaseConnector::createConnectionDetails()`: `NULL`) Optional; In congruence with `cdmSchema` and `resultSchema`. Ignores `cdm`.
`cdmSchema`	(`character(1)`: `NULL`) Optional; In congruence with `connectionDetails` and `resultSchema`. Ignores `cdm`.
`resultSchema`	(`character(1)`: `NULL`) Optional; In congruence with `connectionDetails` and `cdmSchema`. Ignores `cdm`.
`tempEmulationSchema`	(`character(1)`) Schema to emulate temp tables.
`minEraDuration`	(`integer(1)`: `0`) Minimum time an event era should last to be included in analysis
`eraCollapseSize`	(`integer(1)`: `30`) Window of time between which two eras of the same event cohort are collapsed into one era
`combinationWindow`	(`integer(1)`: `30`) Window of time two event cohorts need to overlap to be considered a combination treatment
`minCellCount`	(`integer(1)`: `5`) Minimum count required per pathway. Censors data below `x` as `⁠<x⁠`. This minimum value will carry over to the sankey diagram and sunburst plot.

Value

(invisible(NULL))

Examples


ableToRun <- all(
  require("CirceR", character.only = TRUE, quietly = TRUE),
  require("CDMConnector", character.only = TRUE, quietly = TRUE),
  require("TreatmentPatterns", character.only = TRUE, quietly = TRUE),
  require("dplyr", character.only = TRUE, quietly = TRUE)
)

if (require("CirceR", character.only = TRUE, quietly = TRUE)) {
  library(TreatmentPatterns)
  library(CDMConnector)
  library(dplyr)

  withr::local_envvar(
    R_USER_CACHE_DIR = tempfile(),
    EUNOMIA_DATA_FOLDER = Sys.getenv("EUNOMIA_DATA_FOLDER", unset = tempfile())
  )

  tryCatch({
    if (Sys.getenv("skip_eunomia_download_test") != "TRUE") {
      CDMConnector::downloadEunomiaData(overwrite = TRUE)
    }
  },
  error = function(e) NA)

  con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir())
  cdm <- cdmFromCon(con, cdmSchema = "main", writeSchema = "main")

  cohortSet <- readCohortSet(
    path = system.file(package = "TreatmentPatterns", "exampleCohorts")
  )

  cdm <- generateCohortSet(
    cdm = cdm,
    cohortSet = cohortSet,
    name = "cohort_table"
  )

  cohorts <- cohortSet %>%
    # Remove 'cohort' and 'json' columns
    select(-"cohort", -"json") %>%
    mutate(type = c("event", "event", "event", "event", "exit", "event", "event", "target")) %>%
    rename(
      cohortId = "cohort_definition_id",
      cohortName = "cohort_name",
    ) %>%
    select("cohortId", "cohortName", "type")

  executeTreatmentPatterns(
    cohorts = cohorts,
    cohortTableName = "cohort_table",
    cdm = cdm,
    outputPath = tempdir()
  )
    
  DBI::dbDisconnect(con, shutdown = TRUE)
}

ableToRun <- all(
  require("CirceR", character.only = TRUE, quietly = TRUE),
  require("CDMConnector", character.only = TRUE, quietly = TRUE),
  require("TreatmentPatterns", character.only = TRUE, quietly = TRUE),
  require("dplyr", character.only = TRUE, quietly = TRUE)
)

if (require("CirceR", character.only = TRUE, quietly = TRUE)) {
  library(TreatmentPatterns)
  library(CDMConnector)
  library(dplyr)

  withr::local_envvar(
    R_USER_CACHE_DIR = tempfile(),
    EUNOMIA_DATA_FOLDER = Sys.getenv("EUNOMIA_DATA_FOLDER", unset = tempfile())
  )

  tryCatch({
    if (Sys.getenv("skip_eunomia_download_test") != "TRUE") {
      CDMConnector::downloadEunomiaData(overwrite = TRUE)
    }
  },
  error = function(e) NA)

  con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir())
  cdm <- cdmFromCon(con, cdmSchema = "main", writeSchema = "main")

  cohortSet <- readCohortSet(
    path = system.file(package = "TreatmentPatterns", "exampleCohorts")
  )

  cdm <- generateCohortSet(
    cdm = cdm,
    cohortSet = cohortSet,
    name = "cohort_table"
  )

  cohorts <- cohortSet %>%
    # Remove 'cohort' and 'json' columns
    select(-"cohort", -"json") %>%
    mutate(type = c("event", "event", "event", "event", "exit", "event", "event", "target")) %>%
    rename(
      cohortId = "cohort_definition_id",
      cohortName = "cohort_name",
    ) %>%
    select("cohortId", "cohortName", "type")

  executeTreatmentPatterns(
    cohorts = cohorts,
    cohortTableName = "cohort_table",
    cdm = cdm,
    outputPath = tempdir()
  )
    
  DBI::dbDisconnect(con, shutdown = TRUE)
}

export

Description

Export andromeda generated by computePathways object to sharable csv-files and/or a zip archive.

Usage

export(
  andromeda,
  outputPath,
  ageWindow = 10,
  minCellCount = 5,
  censorType = "minCellCount",
  archiveName = NULL
)
export(
  andromeda,
  outputPath,
  ageWindow = 10,
  minCellCount = 5,
  censorType = "minCellCount",
  archiveName = NULL
)

Arguments

`andromeda`	(`Andromeda::andromeda()`) Andromeda object.
`outputPath`	(`character(1)`)
`ageWindow`	(`integer(n)`: `10`) Number of years to bin age groups into. It may also be a vector of integers. I.e. `c(0, 18, 150)` which will results in age group `0-18` which includes subjects `⁠< 19⁠`. And age group `18-150` which includes subjects `⁠> 18⁠`.
`minCellCount`	(`integer(1)`: `5`) Minimum count required per pathway. Censors data below `x` as `⁠<x⁠`. This minimum value will carry over to the sankey diagram and sunburst plot.
`censorType`	(`character(1)`) `"minCellCount"` Censors pathways <`minCellCount` to `minCellCount`. `"remove"` Censors pathways <`minCellCount` by removing them completely. `"mean"` Censors pathways <`minCellCount` to the mean of all frequencies below `minCellCount`
`archiveName`	(`character(1)`: `NULL`) If not `NULL` adds the exported files to a ZIP-file with the specified archive name.

Value

(invisible(NULL))

Examples


ableToRun <- all(
  require("CirceR", character.only = TRUE, quietly = TRUE),
  require("CDMConnector", character.only = TRUE, quietly = TRUE),
  require("TreatmentPatterns", character.only = TRUE, quietly = TRUE),
  require("dplyr", character.only = TRUE, quietly = TRUE)
)

if (ableToRun) {
  library(TreatmentPatterns)
  library(CDMConnector)
  library(dplyr)

  withr::local_envvar(
    R_USER_CACHE_DIR = tempfile(),
    EUNOMIA_DATA_FOLDER = Sys.getenv("EUNOMIA_DATA_FOLDER", unset = tempfile())
  )

  tryCatch({
    if (Sys.getenv("skip_eunomia_download_test") != "TRUE") {
      CDMConnector::downloadEunomiaData(overwrite = TRUE)
    }
  }, error = function(e) NA)

  con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir())
  cdm <- cdmFromCon(con, cdmSchema = "main", writeSchema = "main")

  cohortSet <- readCohortSet(
    path = system.file(package = "TreatmentPatterns", "exampleCohorts")
  )

  cdm <- generateCohortSet(
    cdm = cdm,
    cohortSet = cohortSet,
    name = "cohort_table"
  )

  cohorts <- cohortSet %>%
    # Remove 'cohort' and 'json' columns
    select(-"cohort", -"json") %>%
    mutate(type = c("event", "event", "event", "event", "exit", "event", "event", "target")) %>%
    rename(
      cohortId = "cohort_definition_id",
      cohortName = "cohort_name",
    ) %>%
    select("cohortId", "cohortName", "type")

  outputEnv <- computePathways(
    cohorts = cohorts,
    cohortTableName = "cohort_table",
    cdm = cdm
  )

  export(
    andromeda = outputEnv,
    outputPath = tempdir()
  )

  Andromeda::close(outputEnv)
  DBI::dbDisconnect(con, shutdown = TRUE)
}

ableToRun <- all(
  require("CirceR", character.only = TRUE, quietly = TRUE),
  require("CDMConnector", character.only = TRUE, quietly = TRUE),
  require("TreatmentPatterns", character.only = TRUE, quietly = TRUE),
  require("dplyr", character.only = TRUE, quietly = TRUE)
)

if (ableToRun) {
  library(TreatmentPatterns)
  library(CDMConnector)
  library(dplyr)

  withr::local_envvar(
    R_USER_CACHE_DIR = tempfile(),
    EUNOMIA_DATA_FOLDER = Sys.getenv("EUNOMIA_DATA_FOLDER", unset = tempfile())
  )

  tryCatch({
    if (Sys.getenv("skip_eunomia_download_test") != "TRUE") {
      CDMConnector::downloadEunomiaData(overwrite = TRUE)
    }
  }, error = function(e) NA)

  con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir())
  cdm <- cdmFromCon(con, cdmSchema = "main", writeSchema = "main")

  cohortSet <- readCohortSet(
    path = system.file(package = "TreatmentPatterns", "exampleCohorts")
  )

  cdm <- generateCohortSet(
    cdm = cdm,
    cohortSet = cohortSet,
    name = "cohort_table"
  )

  cohorts <- cohortSet %>%
    # Remove 'cohort' and 'json' columns
    select(-"cohort", -"json") %>%
    mutate(type = c("event", "event", "event", "event", "exit", "event", "event", "target")) %>%
    rename(
      cohortId = "cohort_definition_id",
      cohortName = "cohort_name",
    ) %>%
    select("cohortId", "cohortName", "type")

  outputEnv <- computePathways(
    cohorts = cohorts,
    cohortTableName = "cohort_table",
    cdm = cdm
  )

  export(
    andromeda = outputEnv,
    outputPath = tempdir()
  )

  Andromeda::close(outputEnv)
  DBI::dbDisconnect(con, shutdown = TRUE)
}

InputHandler

Description

Class to handle input from the user. Supports direct paths or input fields through setDataPath().

Super class

TreatmentPatterns::ShinyModule -> InputHandler

Active bindings

reactiveValues: (reactiveValues)
reactiveValues class created by reactiveValues.

Methods

Public methods

InputHandler$uiMenu()
InputHandler$uiBody()
InputHandler$server()
InputHandler$uiDatabaseSelector()
InputHandler$setDataPath()
InputHandler$clone()

Inherited methods

TreatmentPatterns::ShinyModule$initialize()
TreatmentPatterns::ShinyModule$validate()

Method `uiMenu()`

Method to include a menuItem to link to the body.

Usage

InputHandler$uiMenu(label = "File upload", tag = "fileUpload")

Arguments

label: (character(1))
Label to show for the menuItem.
tag: (character(1))
Tag to use internally in input.

Returns

(menuItem)

Method `uiBody()`

Method to include a tabItem to include the body.

Usage

InputHandler$uiBody()

Returns

(tabItem)

Method `server()`

Method to handle the back-end.

Usage

InputHandler$server(input, output, session)

Arguments

input: (input)
Input from the server function.
output: (output)
Output from the server function.
session: (session)
Session from the server function.

Returns

(NULL)

Method `uiDatabaseSelector()`

Method to include a uiOutput to select between multiple uploaded files.

Usage

InputHandler$uiDatabaseSelector()

Returns

(uiOutput)

Method `setDataPath()`

Method to dictate where the data is coming from, either from the input through the shiny application, or from a specified path. When one is provided, the other is ignored.

Usage

InputHandler$setDataPath(tag = "uploadField", input = NULL, path = NULL)

Arguments

tag: (character(1))
Tag to use internally in input.
input: (input)
Input from the server function of the shiny app.
path: (character(1))
Path to a zip-file containing TreatmentPatterns output files.

Returns

(invisible(self))

Method `clone()`

The objects of this class are cloneable with this method.

Usage

InputHandler$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

launchResultsExplorer

Description

Launches the ResultExplorer shinyApp.

Usage

launchResultsExplorer()
launchResultsExplorer()

Value

(shinyApp)

Examples

if (interactive()) {
  launchResultsExplorer()
}
if (interactive()) {
  launchResultsExplorer()
}

SankeyDiagram

Description

Class to handle the Sankey diagram of TreatmentPatterns.

Super classes

TreatmentPatterns::ShinyModule -> TreatmentPatterns::InteracitvePlot -> SankeyDiagram

Methods

Public methods

SankeyDiagram$clone()

Inherited methods

TreatmentPatterns::ShinyModule$validate()
TreatmentPatterns::InteracitvePlot$initialize()
TreatmentPatterns::InteracitvePlot$server()
TreatmentPatterns::InteracitvePlot$uiBody()
TreatmentPatterns::InteracitvePlot$uiMenu()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

SankeyDiagram$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

ShinyModule

Description

ShinyModule super class

Active bindings

namespace: Namespace of the module.

Methods

Public methods

ShinyModule$new()
ShinyModule$validate()
ShinyModule$uiMenu()
ShinyModule$uiBody()
ShinyModule$server()
ShinyModule$clone()

Method `new()`

Initializer method

Usage

ShinyModule$new(namespace)

Arguments

namespace: (character(1))

Returns

(invisible(self))

Method `validate()`

Validator method

Usage

ShinyModule$validate()

Returns

(invisible(self))

Method `uiMenu()`

Method to include a menuItem to link to the body.

Usage

ShinyModule$uiMenu(label, tag)

Arguments

label: (character(1))
Label to show for the menuItem.
tag: (character(1))
Tag to use internally in input.

Returns

(menuItem)

Method `uiBody()`

Method to include a tabItem to include the body.

Usage

ShinyModule$uiBody()

Returns

(tabItem)

Method `server()`

Method to handle the back-end.

Usage

ShinyModule$server(input, output, session)

Arguments

input: (input)
Input from the server function.
output: (output)
Output from the server function.
session: (session)
Session from the server function.

Returns

(NULL)

Method `clone()`

The objects of this class are cloneable with this method.

Usage

ShinyModule$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

SunburstPlot

Description

Class to handle the Sunburst plot of TreatmentPatterns.

Super classes

TreatmentPatterns::ShinyModule -> TreatmentPatterns::InteracitvePlot -> SunburstPlot

Methods

Public methods

SunburstPlot$clone()

Inherited methods

TreatmentPatterns::ShinyModule$validate()
TreatmentPatterns::InteracitvePlot$initialize()
TreatmentPatterns::InteracitvePlot$server()
TreatmentPatterns::InteracitvePlot$uiBody()
TreatmentPatterns::InteracitvePlot$uiMenu()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

SunburstPlot$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Package 'TreatmentPatterns'

Help Index

CharacterizationPlots

Description

Super class

Methods

Public methods

Method uiMenu()

Usage

Arguments

Returns

Method uiBody()

Usage

Returns

Method server()

Usage

Arguments

Returns

Method clone()

Usage

Arguments

computePathways

Description

Usage

Arguments

Value

Examples

createSankeyDiagram

Description

Usage

Arguments

Value

Examples

createSankeyDiagram2

Description

Usage

Arguments

Value

Examples

createSunburstPlot

Description

Usage

Arguments

Value

Examples

createSunburstPlot2

Description

Usage

Arguments

Value

Examples

executeTreatmentPatterns

Description

Usage

Arguments

Value

Examples

export

Description

Usage

Arguments

Value

Examples

InputHandler

Description

Super class

Active bindings

Methods

Public methods

Method uiMenu()

Usage

Arguments

Returns

Method uiBody()

Usage

Returns

Method server()

Usage

Arguments

Returns

Method `uiMenu()`

Method `uiBody()`

Method `server()`

Method `clone()`

Method `uiMenu()`

Method `uiBody()`

Method `server()`

Method `uiDatabaseSelector()`

Method `setDataPath()`

Method `clone()`

Method `clone()`

Method `new()`

Method `validate()`

Method `uiMenu()`

Method `uiBody()`

Method `server()`

Method `clone()`

Method `clone()`