export

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

Usage

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

Arguments

andromeda

(Andromeda::andromeda()) Andromeda object.

outputPath

(character: NULL) Output path where to write output files to. When set to NULL no files will be written, and only the results object is returned.

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.

nonePaths

(logical(1)) Should None paths be included? This will fetch all persons included in the target cohort and assign them a "None" pathway. Significantly impacts performance.

stratify

(logical(1)) Should pathways be stratified? This will perform pairwise stratification between age, sex, and index year. Significantly impacts performance.

Value

TreatmentPatternsResults object

Examples

# \donttest{
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
  )

  results <- export(
    andromeda = outputEnv
  )

  Andromeda::close(outputEnv)
  DBI::dbDisconnect(con, shutdown = TRUE)
}
#> 
#> Download completed!
#> Creating CDM database /tmp/RtmpsdqEev/file25072c2c4a9b/GiBleed_5.3.zip
#> ! cdm name not specified and could not be inferred from the cdm source table
#> ℹ Generating 8 cohorts
#> ℹ Generating cohort (1/8) - acetaminophen
#> ✔ Generating cohort (1/8) - acetaminophen [159ms]
#> 
#> ℹ Generating cohort (2/8) - amoxicillin
#> ✔ Generating cohort (2/8) - amoxicillin [146ms]
#> 
#> ℹ Generating cohort (3/8) - aspirin
#> ✔ Generating cohort (3/8) - aspirin [154ms]
#> 
#> ℹ Generating cohort (4/8) - clavulanate
#> ✔ Generating cohort (4/8) - clavulanate [146ms]
#> 
#> ℹ Generating cohort (5/8) - death
#> ✔ Generating cohort (5/8) - death [122ms]
#> 
#> ℹ Generating cohort (6/8) - doxylamine
#> ✔ Generating cohort (6/8) - doxylamine [137ms]
#> 
#> ℹ Generating cohort (7/8) - penicillinv
#> ✔ Generating cohort (7/8) - penicillinv [144ms]
#> 
#> ℹ Generating cohort (8/8) - viralsinusitis
#> ✔ Generating cohort (8/8) - viralsinusitis [206ms]
#> 
#> -- Qualifying records for cohort definitions: 1, 2, 3, 4, 5, 6, 7, 8
#> 	Records: 14041
#> 	Subjects: 2693
#> -- Removing records < minEraDuration (0)
#> 	Records: 11386
#> 	Subjects: 2159
#> >> Starting on target: 8 (viralsinusitis)
#> -- Removing events where index date < target index date + indexDateOffset (0)
#> 	Records: 8381
#> 	Subjects: 2159
#> -- splitEventCohorts
#> 	Records: 8366
#> 	Subjects: 2144
#> -- Collapsing eras, eraCollapse (30)
#> 	Records: 8366
#> 	Subjects: 2144
#> -- Iteration 1: minPostCombinationDuration (30), combinatinoWindow (30)
#> 	Records: 558
#> 	Subjects: 512
#> -- Iteration 2: minPostCombinationDuration (30), combinatinoWindow (30)
#> 	Records: 554
#> 	Subjects: 512
#> -- After Combination
#> 	Records: 554
#> 	Subjects: 512
#> -- filterTreatments (First)
#> 	Records: 553
#> 	Subjects: 512
#> -- treatment construction done
#> 	Records: 553
#> 	Subjects: 512
# }