Extract diabetes status prior to an index date.

Usage

extract_diabetes(
  cohort,
  varname = NULL,
  codelist_type1 = NULL,
  codelist_type2 = NULL,
  codelist_type1_vector = NULL,
  codelist_type2_vector = NULL,
  indexdt,
  t = NULL,
  t_varname = TRUE,
  db_open = NULL,
  db = NULL,
  db_filepath = NULL,
  out_save_disk = FALSE,
  out_subdir = NULL,
  out_filepath = NULL,
  return_output = TRUE
)

Arguments

cohort: Cohort to extract age for.
varname: Optional name for variable in output dataset.
codelist_type1: Name of codelist (stored on hard disk in "codelists/analysis/") for type 1 diabetes to query the database with.
codelist_type2: Name of codelist (stored on hard disk in "codelists/analysis/") for type 2 diabetes to query the database with.
codelist_type1_vector: Vector of codes for type 1 diabetes to query the database with.
codelist_type2_vector: Vector of codes for type 2 diabetes to query the database with.
indexdt: Name of variable which defines index date in cohort.
t: Number of days after index date at which to calculate variable.
t_varname: Whether to add t to varname.
db_open: An open SQLite database connection created using RSQLite::dbConnect, to be queried.
db: Name of SQLITE database on hard disk (stored in "data/sql/"), to be queried.
db_filepath: Full filepath to SQLITE database on hard disk, to be queried.
out_save_disk: If TRUE will attempt to save outputted data frame to directory "data/extraction/".
out_subdir: Sub-directory of "data/extraction/" to save outputted data frame into.
out_filepath: Full filepath and filename to save outputted data frame into.
return_output: If TRUE will return outputted data frame into R workspace.

Value

A data frame with variable diabetes status.

Details

If an individual is found to have medical codes for both type 1 and type 2 diabetes, the returned value of diabetes status will be type 1 diabetes. Full details on the algorithm for extracting diabetes status are given in the vignette: Details-on-algorithms-for-extracting-specific-variables. This vignette can be viewed by running vignette("help", package = "rcprd").

Specifying db requires a specific underlying directory structure. The SQLite database must be stored in "data/sql/" relative to the working directory. If the SQLite database is accessed through db, the connection will be opened and then closed after the query is complete. The same is true if the database is accessed through db_filepath. A connection to the SQLite database can also be opened manually using RSQLite::dbConnect, and then using the object as input to parameter db_open. After wards, the connection must be closed manually using RSQLite::dbDisconnect. If db_open is specified, this will take precedence over db or db_filepath.

If out_save_disk = TRUE, the data frame will automatically be written to an .rds file in a subdirectory "data/extraction/" of the working directory. This directory structure must be created in advance. out_subdir can be used to specify subdirectories within "data/extraction/". These options will use a default naming convetion. This can be overwritten using out_filepath to manually specify the location on the hard disk to save. Alternatively, return the data frame into the R workspace using return_output = TRUE and then save onto the hard disk manually.

Specifying the non-vector type codelists requires a specific underlying directory structure. The codelist on the hard disk must be stored in "codelists/analysis/" relative to the working directory, must be a .csv file, and contain a column "medcodeid", "prodcodeid" or "ICD10" depending on the chosen tab. The input to these variables should just be the name of the files (excluding the suffix .csv). The codelists can also be read in manually, and supplied as a character vector. This option will take precedence over the codelists stored on the hard disk if both are specified.

Examples


## Connect
aurum_extract <- connect_database(file.path(tempdir(), "temp.sqlite"))

## Create SQLite database using cprd_extract
cprd_extract(aurum_extract,
filepath = system.file("aurum_data", package = "rcprd"),
filetype = "observation", use_set = FALSE)
#> 
  |                                                                            
  |                                                                      |   0%
#> Adding /home/runner/work/_temp/Library/rcprd/aurum_data/aurum_allpatid_set1_extract_observation_001.txt 2024-11-14 15:23:42.857534
#> 
  |                                                                            
  |=======================                                               |  33%
#> Adding /home/runner/work/_temp/Library/rcprd/aurum_data/aurum_allpatid_set1_extract_observation_002.txt 2024-11-14 15:23:42.869881
#> 
  |                                                                            
  |===============================================                       |  67%
#> Adding /home/runner/work/_temp/Library/rcprd/aurum_data/aurum_allpatid_set1_extract_observation_003.txt 2024-11-14 15:23:42.880972
#> 
  |                                                                            
  |======================================================================| 100%

## Define cohort and add index date
pat<-extract_cohort(system.file("aurum_data", package = "rcprd"))
pat$indexdt <- as.Date("01/01/1955", format = "%d/%m/%Y")

## Extract diabetes prior to index date
extract_diabetes(cohort = pat,
codelist_type1_vector = "498521000006119",
codelist_type2_vector = "401539014",
indexdt = "indexdt",
db_open = aurum_extract)
#>    patid diabetes
#> 1      1    Type1
#> 2      2   Absent
#> 3      3   Absent
#> 4      4   Absent
#> 5      5    Type1
#> 6      6   Absent
#> 7      7   Absent
#> 8      8   Absent
#> 9      9   Absent
#> 10    10   Absent
#> 11    11   Absent
#> 12    12   Absent

## clean up
RSQLite::dbDisconnect(aurum_extract)
unlink(file.path(tempdir(), "temp.sqlite"))