Extract smoking status prior to index date.

Extract smoking status prior to index date.

Usage

extract_smoking(
  cohort,
  varname = NULL,
  codelist_non = NULL,
  codelist_ex = NULL,
  codelist_light = NULL,
  codelist_mod = NULL,
  codelist_heavy = NULL,
  codelist_non_vector = NULL,
  codelist_ex_vector = NULL,
  codelist_light_vector = NULL,
  codelist_mod_vector = NULL,
  codelist_heavy_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_non

Name of codelist (stored on hard disk in "codelists/analysis/") for non-smoker to query the database with.

codelist_ex

Name of codelist (stored on hard disk in "codelists/analysis/") for ex-smoker to query the database with.

codelist_light

Name of codelist (stored on hard disk in "codelists/analysis/") for light smoker to query the database with.

codelist_mod

Name of codelist (stored on hard disk in "codelists/analysis/") for moderate smoker to query the database with.

codelist_heavy

Name of codelist (stored on hard disk in "codelists/analysis/") for heavy smoker to query the database with.

codelist_non_vector

Vector of codes for non-smoker to query the database with.

codelist_ex_vector

Vector of codes for ex-smoker to query the database with.

codelist_light_vector

Vector of codes for light smoker to query the database with.

codelist_mod_vector

Vector of codes for moderate smoker to query the database with.

codelist_heavy_vector

Vector of codes for heavy smoker 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 smoking status.

Details

Returns the most recent value of smoking status. If the most recently recorded observation of smoking status is non-smoker, but the individual has a history of smoking identified through the medical record, the outputted value of smoking status will be ex-smoker. Full details on the algorithm for extracting smoking 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.

We take the most recent smoking status record. If an individuals most recent smoking status is a non-smoker, but they have a history of smoking prior to this, these individuals will be classed as ex-smokers.

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 15:23:43.354545
#> 
  |                                                                            
  |=======================                                               |  33%
#> Adding /home/runner/work/_temp/Library/rcprd/aurum_data/aurum_allpatid_set1_extract_observation_002.txt 2024-11-14 15:23:43.366902
#> 
  |                                                                            
  |===============================================                       |  67%
#> Adding /home/runner/work/_temp/Library/rcprd/aurum_data/aurum_allpatid_set1_extract_observation_003.txt 2024-11-14 15:23:43.377681
#> 
  |                                                                            
  |======================================================================| 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 smoking status prior to index date
extract_smoking(cohort = pat,
codelist_non_vector = "498521000006119",
codelist_ex_vector = "401539014",
codelist_light_vector = "128011000000115",
codelist_mod_vector = "380389013",
codelist_heavy_vector = "13483031000006114",
indexdt = "indexdt",
db_open = aurum_extract)
#>    patid    smoking
#> 1      1      Heavy
#> 2     10       <NA>
#> 3     11       <NA>
#> 4     12       <NA>
#> 5      2 Non-smoker
#> 6      3       <NA>
#> 7      4   Moderate
#> 8      5  Ex-smoker
#> 9      6   Moderate
#> 10     7       <NA>
#> 11     8       <NA>
#> 12     9       <NA>

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