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#' @title Score the EORTC QLQ-PAN26 Quality of Life Questionnaire
#'
#' @description Scores the European Organization for Research and Treatment of
#' Cancer (EORTC) QLQ-PAN26 Pancreatic Cancer Module. (Experimental: This
#' function was written quickly... please hand score 1 or 2 patients and check
#' for accuracy)
#'
#' @param df A data frame containing responses to the 26 QLQ-PAN26 items, and
#' possibly other variables.
#' @param items A character vector with the QLQ-PAN26 item names, or a numeric
#' vector indicating the column numbers of the QLQ-PAN26 items in \code{df}.
#' If \code{items} is omitted, then \code{qlq_PAN26} will assume that
#' \code{df} contains \strong{ONLY} the QLQ-PAN26 items and no other variables.
#' See Details for more information.
#' @param keepNvalid Logical, whether to return variables containing the
#' number of valid, non-missing items on each scale for each respondent should
#' be returned in the data frame with the scale scores. The default is
#' \code{FALSE}. Set to \code{TRUE} to return these variables, which will be
#' named \code{"scalename_N"} (e.g., \code{QL_N}). Most users should omit
#' this argument entirely. This argument might be removed from future
#' versions of the package, so please let me know if you think this argument
#' useful and would rather it remain a part of the function.
#'
#'
#' @details
#' This function returns a total of 17 different scores from the EORTC
#' QLQ-PAN26. Scores are calculated according to the official scoring algorithms
#' from the EORTC. At the time this scoring function was written (April 2022),
#' the QLQ-PAN26 had completed Phase 3 testing; however, the official scoring
#' instructions from the EORTC warned that this scaling structure is still
#' preliminary and may change in the future.
#'
#' In addition to the name of your data frame containing the QLQ-PAN26 item
#' responses (\code{df}), you need to tell the function how to find the
#' variables that correspond to the QLQ-PAN26 items in \code{df}. You can do this
#' in 1 of 2 ways:
#' \enumerate{
#' \item The first way is to manually provide the item names or locations
#' using the \code{items} argument. For example, if your first 10
#' variables in \code{df} contain demographics, followed by the 26 QLQ-PAN26
#' items \strong{in order} starting with the 11th variable, then you could
#' use \code{items = 11:36}.
#' \item The second way only applies if your data frame (\code{df}) contains
#' \strong{ONLY} the 26 variables corresponding to the 26 QLQ-PAN26 items,
#' in order, with no other non-QLQ-PAN26 variables. In this case, you can
#' just use the \code{df} argument and omit \code{items}.
#' }
#'
#'
#' @note
#' \strong{As of April 15, 2022, there is an error in the official PAN26 scoring
#' instructions from the EORTC.}
#' The first page of the official PAN26 scoring instructions from the EORTC
#' indicates that the two items comprising the SA (Satisfaction with health
#' care) functional scale should be reverse scored. In the instructions, these
#' two items are numbered as 53 and 54. However, the second page of the
#' instructions indicates items 55 and 56 should be reversed (see bullet, "(1)
#' Raw score" in the "Principle for scoring" subsection). Items 55 and 56
#' comprise the SX (Sexuality) functional scale.
#' \strong{The items that need to be reversed are the SX items (55 and 56), NOT
#' the SA items (not 53 and 54).} This function correctly reverses the SX items
#' instead of the SA items.
#'
#' @section How Missing Data is Handled:
#' The \code{qlq_PAN26} function will calculate the scale scores as long as at
#' least half of the items on the given scale have valid, non-missing item
#' responses. Scores calculated in the presence of missing
#' items are pro-rated so that their theoretical minimum and maximum values
#' are identical to those from scores calculated from complete data.
#'
#'
#' @return
#' A data frame with all 17 of the QLQ-PAN26 scores is returned. Of the 17
#' scores, 15 are Symptom Scales and 2 are Functional Scales (see below). Of
#' the 15 Symptom Scales, 10 are based on a single item each. All scores are
#' scaled to range from 0-100, even scores based on single items. Be aware that
#' these single-item scales still have only 4 possible values, even though they
#' are transformed to range from 0-100. The scale names and numbers of items are
#' listed below.
#'
#' \strong{Symptom Scales (higher is more symptoms, worse functioning)}
#' \itemize{
#' \item \strong{PAN_PP} - Pancreatic pain (from 4 items)
#' \item \strong{PAN_BF} - Bloating (from 1 item)
#' \item \strong{PAN_DS} - Digestive symptoms (from 2 items)
#' \item \strong{PAN_TA} - Taste (from 1 item)
#' \item \strong{PAN_ID} - Indigestion (from 1 item)
#' \item \strong{PAN_FL} - Flatulence (from 1 item)
#' \item \strong{PAN_WL} - Weight loss (from 1 item)
#' \item \strong{PAN_WE} - Weakness arms and legs (from 1 item)
#' \item \strong{PAN_DM} - Dry mouth (from 1 item)
#' \item \strong{PAN_LI} - Hepatic symptoms (from 2 items)
#' \item \strong{PAN_BO} - Altered bowel habit (from 2 items)
#' \item \strong{PAN_BI} - Body image (from 2 item)
#' \item \strong{PAN_SE} - Troubled with side-effects(from 1 item)
#' \item \strong{PAN_FU} - Future Worries (from 1 item)
#' \item \strong{PAN_PL} - Planning of activities (from 1 item)
#' }
#'
#' \strong{Functional Scales (higher is better functioning)}
#' \itemize{
#' \item \strong{PAN_SA} - Satisfaction with health care (from 2 items)
#' \item \strong{PAN_SX} - Sexuality (from 2 items)
#' }
#'
#' Optionally, the data frame can additionally have variables containing the
#' number of valid item responses on each scale for each respondent (if
#' \code{keepNvalid = TRUE}, but this option might be removed in future package
#' updates).
#'
#' @references
#'
#' Fitzsimmons D, Johnson CD, George S, et al. Development of a disease specific
#' quality of life (QoL) questionnaire module to supplement the EORTC core
#' cancer QoL questionnaire, the QLQ-PAN26 in patients with pancreatic cancer.
#' \emph{Eur. J. Cancer 35}: 939-941, 1999.
#'
#'
#'
#' @export
#'
#' @examples
#'
#' \dontrun{
#' dat <- PROscorerTools::makeFakeData(n = 10, nitems = 26, prefix = "pan", values = 1:4)
#' qlq_pan26(dat, items = 1:26)
#' }
qlq_pan26 <- function(df, items = NULL, keepNvalid = FALSE) {
# Check arguments that are unique to qlq_pan26,
# or that require additional checks not already done by scoreScale().
# Get dfItems, a df with only the items -------------------------------------
dfItems <- PROscorerTools::get_dfItems(df, items )
# Check that dfItems has 26 items
if (!PROscorerTools::chk_nitems(dfItems, 26)) {
stop(paste(strwrap(
"The QLQ-PAN26 has 26 items, but the function found a different number
of items given the arguments and data you supplied.",
exdent = 2, width = 65), collapse = "\n"))
}
## Check item ranges:
## Not strictly necessary since scoreScale() will check, too.
## However, the scoreScale() error msgs would be confusing.
## Best to check ranges and give helpful error msgs for specific questionnaires.
if (!PROscorerTools::chk_values(dfItems = dfItems[1:26],
values = c(1:4, NA))) {
stop(paste(strwrap(
"At least one of your items has a value that is not allowed.
Items should have all integer values between 1 and 4,
or NA (i.e., missing).",
exdent = 2, width = 65), collapse = "\n"))
}
##----------------------------------------------------------------------------##
##----------------------------------------------------------------------------##
## Warn if any items do not contain the full range of possible values
## Below was in PROscorerTools::chkstop_minmax(), but doesn't make sense when
## triggered inside a PROscorer function. Write a custom check for
## qlq_c30 for now, and later write helper fn to do this more seamlessly.
##----------------------------------------------------------------------------##
##----------------------------------------------------------------------------##
# if (imin < min(dfItems, na.rm = TRUE) ) {
# warning(paste(strwrap(sprintf(
# "The lower bound you gave to 'minmax', %s, is smaller than the
# minimum item response observed in the data you provided to 'df'.
# Please double-check that you gave the correct lower bound to 'minmax'
# (it should be the value of the lowest possible item response),
# and that the item responses are coded correctly in your data.
# If both are correct, you can ignore this warning.", imin),
# exdent = 2),
# collapse = "\n"))
# }
# if (imax > max(dfItems, na.rm = TRUE) ) {
# warning(paste(strwrap(sprintf(
# "The upper bound you gave to 'minmax', %s, is larger than the
# largest item response observed in the data you provided to 'df'.
# Please double-check that you gave the correct upper bound to 'minmax'
# (it should be the value of the highest possible item response),
# and that the item responses are coded correctly in your data.
# If both are correct, you can ignore this warning.", imax),
# exdent = 2),
# collapse = "\n"))
# }
##----------------------------------------------------------------------------##
##----------------------------------------------------------------------------##
PAN_PP <- PROscorerTools::scoreScale( df = dfItems, okmiss = .50,
keepNvalid = keepNvalid,
minmax = c(1, 4),
items = c(1, 3:5),
revitems = FALSE,
scalename ="PAN_PP" )
PAN_BF <- PROscorerTools::scoreScale( df = dfItems, okmiss = .50,
keepNvalid = keepNvalid,
minmax = c(1, 4),
items = 2,
revitems = FALSE,
scalename ="PAN_BF" )
PAN_DS <- PROscorerTools::scoreScale( df = dfItems, okmiss = .50,
keepNvalid = keepNvalid,
minmax = c(1, 4),
items = 6:7,
revitems = FALSE,
scalename ="PAN_DS" )
PAN_TA <- PROscorerTools::scoreScale( df = dfItems, okmiss = .50,
keepNvalid = keepNvalid,
minmax = c(1, 4),
items = 8,
revitems = FALSE,
scalename ="PAN_TA" )
PAN_ID <- PROscorerTools::scoreScale( df = dfItems, okmiss = .50,
keepNvalid = keepNvalid,
minmax = c(1, 4),
items = 9,
revitems = FALSE,
scalename ="PAN_ID" )
PAN_FL <- PROscorerTools::scoreScale( df = dfItems, okmiss = .50,
keepNvalid = keepNvalid,
minmax = c(1, 4),
items = 10,
revitems = FALSE,
scalename ="PAN_FL" )
PAN_WL <- PROscorerTools::scoreScale( df = dfItems, okmiss = .50,
keepNvalid = keepNvalid,
minmax = c(1, 4),
items = 11,
revitems = FALSE,
scalename ="PAN_WL" )
PAN_WE <- PROscorerTools::scoreScale( df = dfItems, okmiss = .50,
keepNvalid = keepNvalid,
minmax = c(1, 4),
items = 12,
revitems = FALSE,
scalename ="PAN_WE" )
PAN_DM <- PROscorerTools::scoreScale( df = dfItems, okmiss = .50,
keepNvalid = keepNvalid,
minmax = c(1, 4),
items = 13,
revitems = FALSE,
scalename ="PAN_DM" )
PAN_LI <- PROscorerTools::scoreScale( df = dfItems, okmiss = .50,
keepNvalid = keepNvalid,
minmax = c(1, 4),
items = 14:15,
revitems = FALSE,
scalename ="PAN_LI" )
PAN_BO <- PROscorerTools::scoreScale( df = dfItems, okmiss = .50,
keepNvalid = keepNvalid,
minmax = c(1, 4),
items = 16:17,
revitems = FALSE,
scalename ="PAN_BO" )
PAN_BI <- PROscorerTools::scoreScale( df = dfItems, okmiss = .50,
keepNvalid = keepNvalid,
minmax = c(1, 4),
items = 18:19,
revitems = FALSE,
scalename ="PAN_BI" )
PAN_SE <- PROscorerTools::scoreScale( df = dfItems, okmiss = .50,
keepNvalid = keepNvalid,
minmax = c(1, 4),
items = 20,
revitems = FALSE,
scalename ="PAN_SE" )
PAN_FU <- PROscorerTools::scoreScale( df = dfItems, okmiss = .50,
keepNvalid = keepNvalid,
minmax = c(1, 4),
items = 21,
revitems = FALSE,
scalename ="PAN_FU" )
PAN_PL <- PROscorerTools::scoreScale( df = dfItems, okmiss = .50,
keepNvalid = keepNvalid,
minmax = c(1, 4),
items = 22,
revitems = FALSE,
scalename ="PAN_PL" )
# Functional scales
PAN_SA <- PROscorerTools::scoreScale( df = dfItems, okmiss = .50,
keepNvalid = keepNvalid,
minmax = c(1, 4),
items = 23:24,
revitems = FALSE,
scalename ="PAN_SA" )
PAN_SX <- PROscorerTools::scoreScale( df = dfItems, okmiss = .50,
keepNvalid = keepNvalid,
minmax = c(1, 4),
items = 25:26,
revitems = TRUE,
scalename ="PAN_SX" )
scoreDF <- data.frame(PAN_PP, PAN_BF, PAN_DS, PAN_TA, PAN_ID, PAN_FL, PAN_WL,
PAN_WE, PAN_DM, PAN_LI, PAN_BO, PAN_BI, PAN_SE, PAN_FU,
PAN_PL, PAN_SA, PAN_SX)
return(scoreDF)
}
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