lookupCSCgenderAndAge: Function using gender and age in input file, with a lookup,...

View source: R/lookupCSC2.R

lookupCSCgenderAndAgeR Documentation

Function using gender and age in input file, with a lookup, to get appropriate CSC values

Description

Function using gender and age in input file, with a lookup, to get appropriate CSC values

Usage

lookupCSCgenderAndAge(
  useInternalLookup = TRUE,
  lookupTableName = NULL,
  lookupGenderVarChar,
  lookupAgeVarChar,
  lookupCSCvarChar = "CSC",
  lookupGenderF,
  lookupGenderM,
  lookupGenderO,
  checkInternalLookup = FALSE,
  checkExternalLookup = TRUE,
  dataTableName,
  dataGenderVarChar,
  dataAgeVarChar,
  dataGenderF,
  dataGenderM,
  dataGenderO,
  dataGenderNA = NA_character_,
  dataAgeNA = NA_real_,
  outputCSCvarChar = "CSC",
  lookupRef = "Emily_PhD",
  useClinScoring = FALSE,
  checkData = TRUE,
  overwriteExistingVariable = FALSE,
  showInternalLookup = FALSE
)

Arguments

useInternalLookup

logical: whether to use internal lookup table, defaults to TRUE

lookupTableName

character: name of lookup file to use if not using internal table

lookupGenderVarChar

character: name of gender variable in lookup file

lookupAgeVarChar

character: name of age variable in lookup file

lookupCSCvarChar

character: name of CSC variable in lookup file

lookupGenderF

character: value representing female gender in lookup file

lookupGenderM

character: value representing male gender in lookup file

lookupGenderO

character: value representing other gender in lookup file

checkInternalLookup

logical: whether to print the check for the internal lookup

checkExternalLookup

logical: whether to print the check for an external lookup

dataTableName

character: name of data file to use

dataGenderVarChar

character: value representing female gender in data file

dataAgeVarChar

character: name of gender variable in data file

dataGenderF

value representing female gender in data file

dataGenderM

value representing male gender in data file

dataGenderO

value representing other gender in data file

dataGenderNA

vector of values (one or more) representing missing gender values in datafile

dataAgeNA

vector of values (one or more) representing missing age values in data

outputCSCvarChar

character: name for output CSC variable, defaults to "CSC",

lookupRef

character: which internal referential lookup data to use

useClinScoring

logical: whether to use item mean scoring or "clinical" scoring

checkData

logical: whether to check for issues in the data

overwriteExistingVariable

logical: if TRUE allows overwriting of existing variable, default FALSE

showInternalLookup

logical: if TRUE shows the internal lookup table selected

Value

a tibble containing all the input data with added variable naming lookup used and CSC values

Background

One challenge with YP-CORE, and many other measures, is that the appropriate CSC (Clinically Significant Change) value to use is not the same for all ages and genders. This function takes new data with a gender and an age variable and returns a new tibble with the same data plus the CSC for the gender and age given. It has three lookup tables built into the function but also allows you to submit your own lookup table. Currently, that lookup is expected to be a CSV (comma separated variable) file. I'll improve that to allow a tibble and perhaps other formats.

References/acknowledgements

  1. The default internal lookup is the most recent UK referential data from Emily Blackshaw's PhD. For now, see https://www.coresystemtrust.org.uk/home/instruments/yp-core-information/

  2. The next UK lookup is from Twigg, E., Cooper, M., Evans, C., Freire, E. S., Mellor-Clark, J., McInnes, B., & Barkham, M. (2016). Acceptability, reliability, referential distributions, and sensitivity to change of the YP-CORE outcome measure: Replication and refinement. Child and Adolescent Mental Health, 21(2), 115–123. https://doi.org/10.1111/camh.12128

  3. Currently the only other internal lookup is the Italian data from Di Biase, R., Evans, C., Rebecchi, D., Baccari, F., Saltini, A., Bravi, E., Palmieri, G., & Starace, F. (2021). Exploration of psychometric properties of the Italian version of the Core Young Person’s Clinical Outcomes in Routine Evaluation (YP-CORE). Research in Psychotherapy: Psychopathology, Process and Outcome, 24(2). https://doi.org/10.4081/ripppo.2021.554

History/development log

Version 1: 21.i.2024

Author(s)

Chris Evans

Examples

## Not run: 
### simple usage of the function with comments explaining the arguments rather more
### see Rblog post ... for more information
###
lookupCSCgenderAndAge(useInternalLookup = TRUE, # so using the internal lookup data
                                                # (could have omitted this, it's the default)
   lookupTableName = NULL, # so no need to give an external lookup table name
                                                # (default again could have omitted this)
   lookupGenderVarChar = "Gender", # name of the gender variable in the lookup table
                                                # ditto!
   lookupAgeVarChar = "Age", # name of the age variable ditto
   lookupGenderF = "F", # code for female gender in the lookup table (ditto)
   lookupGenderM = "M", # code for male gender ditto
   lookupGenderO = "O", # code for other gender ditto
                        # for future proofing, current lookup tables are only binary gender
   ### now the arguments about the data to code
   dataTableName = tibData, # crucial name of the data to classify, this and the following
   dataGenderVarChar = "Gender", # name of the gender variable in those data (default)
   dataAgeVarChar = "Age", # you can work out this and the following
   dataGenderF = "F",
   dataGenderM = "M",
   dataGenderO = "O",
   ### no missing values in lookup tables (would be meaningless),
   ### but you may have missing values in your data hence this next argument
   dataGenderNA = NA_character_) -> tibBlackshaw

   ### so that call returns the raw data but now with the CSC values

tibBlackshaw %>%
 group_by(Gender, Age, CSC) %>%
 filter(Dataset2 == "HS" & ID == 1) %>%
 ungroup() %>%
 select(ID, Gender : YPscore, Ref, CSC) %>%
 flextable() %>%
 autofit()

## End(Not run)


cpsyctc/CECPfuns documentation built on May 18, 2024, 11:45 a.m.