R/sx-scoreFACT_ES_BCS.R
In raybaser/FACTscorer: Scores the FACT and FACIT Family of Patient-Reported Outcome Measures
Defines functions
scoreFACT_ES_BCS
Documented in
scoreFACT_ES_BCS
#' @title Score the FACT-ES-BCS
#'
#' @description
#' Generates all of the scores of the Functional Assessment of Cancer Therapy -
#' Endocrine Symptom with Breast Cancer Subscale (FACT-ES-BCS, v4)
#' from item responses.
#'
#' @details
#' Given a data frame that includes all of the FACT-ES-BCS (Version 4) items as
#' variables, appropriately named, this function generates all of the FACT-ES
#' scale scores. It is crucial that the item variables in the supplied data
#' frame are named according to FACT conventions. For example, the first
#' physical well-being item should be named GP1, the second GP2, and so on.
#' Please refer to the materials provided by \url{http://www.facit.org} for the
#' particular questionnaire you are using. In particular, refer to the left
#' margin of the official questionnaire (i.e., from facit.org) for the
#' appropriate item variable names.
#'
#' @section Note:
#' Keep in mind that this function (and R in general) is case-sensitive.
#'
#' All variables should be in numeric or integer format.
#'
#' This scoring function expects missing item responses to be coded as NA,
#' 8, or 9, and valid item responses to be coded as 0, 1, 2, 3, or 4. Any
#' other value for any of the items will result in an error message and no
#' scores.
#'
#' Some item variables are reverse coded for the purpose of generating the
#' scale scores. The official (e.g., from \url{http://www.facit.org}) SAS
#' and SPSS scoring algorithms for this questionnaire automatically replace
#' the original items with their reverse-coded versions. This can be
#' confusing if you accidentally run the algorithm more than once on your
#' data. As its default, \code{scoreFACT_ES} DOES NOT replace any of your
#' original item variables with the reverse coded versions. However, for
#' consistentcy with the behavior of the other versions on
#' \url{http://www.facit.org}, the \code{updateItems} argument is
#' provided. If set to \code{TRUE}, any item that is supposed to be
#' reverse coded will be replaced with its reversed version in the data
#' frame returned by \code{scoreFACT_ES}.
#'
#'
#' @param df A data frame with the FACT-ES items, appropriately-named.
#' @param updateItems Logical, if \code{TRUE} any original item that is
#' reverse coded for scoring will be replaced by its reverse coded version
#' in the returned data frame, and any values of 8 or 9 will be replaced
#' with NA. The default, \code{FALSE}, returns the original items
#' unmodified.
#' @param keepNvalid Logical, if \code{TRUE} the function
#' returns an additional variable for each of the returned scale scores
#' containing the number of valid, non-missing responses from each
#' respondent to the items on the given scale. If \code{FALSE} (the
#' default), these variables are omitted from the returned data frame.
#'
#'
#' @return The original data frame is returned (optionally with modified
#' items if \code{updateItems = TRUE}) with new variables corresponding to
#' the scored scales. If \code{keepNvalid = TRUE}, for each scored scale an
#' additional variable is returned that contains the number of valid
#' responses each respondent made to the items making up the given scale.
#' These optional variables have names of the format \code{SCALENAME_N}.
#' The following scale scores are returned:
#'
#' \describe{
#' \item{PWB}{Physical Well-Being subscale}
#' \item{SWB}{Social/Family Well-Being subscale}
#' \item{EWB}{Emotional Well-Being subscale}
#' \item{FWB}{Physical Well-Being subscale}
#' \item{FACTG}{FACT-G Total Score (i.e., PWB+SWB+EWB+FWB)}
#' \item{ESS_19}{19-item Endocrine Symptom subscale}
#' \item{ESS_23}{23-item Endocrine Symptom subscale}
#' \item{BCS}{Breast Cancer subscale}
#' \item{FACT_ES_TOTAL}{FACT-ES Total Score (i.e., PWB+SWB+EWB+FWB+ESS_19+ESS_23+BCS)}
#' \item{FACT_ES_TOI}{FACT-ES Trial Outcome Index (e.g., PWB+FWB+ESS_19+ESS_23+BCS)}
#' }
#'
#' @references FACT-ES Scoring Guidelines, available at \url{http://www.facit.org}
#'
#' @export
#'
#' @examples
#' ## Setting up item names for fake data
#' G_names <- c(paste0('GP', 1:7),
#' paste0('GS', 1:7),
#' paste0('GE', 1:6),
#' paste0('GF', 1:7))
#' AC_names1 <- c('ES1', 'ES2', 'ES3', 'ES4', 'ES5', 'ES6', 'ES7', 'ES8', 'ES9', 'ES10',
#' 'An9', 'O2', 'C5', 'An10', 'Tax1', 'ES11', 'ES12', 'ES13', 'BRM1')
#' AC_names2 <- c('ES1', 'ES2', 'ES3', 'ES4', 'ES5', 'ES6', 'ES7', 'ES8', 'ES9', 'ES10',
#' 'An9', 'O2', 'C5', 'An10', 'Tax1', 'ES11', 'ES12', 'ES13', 'BRM1',
#' 'GP1', 'GP2', 'GE4', 'GF5')
#' AC_names3 <- c('B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B9')
#' AC_names <- c(AC_names1, AC_names2, AC_names3)
#' itemNames <- c(G_names, AC_names)
#' ## Generating random item responses for 8 fake respondents
#' set.seed(6375309)
#' exampleDat <- t(replicate(8, sample(0:4, size = length(itemNames), replace = TRUE)))
#' ## Making half of respondents missing about 10% of items,
#' ## half missing about 50%.
#' miss10 <- t(replicate(4, sample(c(0, 9), prob = c(0.9, 0.1),
#' size = length(itemNames), replace = TRUE)))
#' miss50 <- t(replicate(4, sample(c(0, 9), prob = c(0.5, 0.5),
#' size = length(itemNames), replace = TRUE)))
#' missMtx <- rbind(miss10, miss50)
#' ## Using 9 as the code for missing responses
#' exampleDat[missMtx == 9] <- 9
#' exampleDat <- as.data.frame(cbind(ID = paste0('ID', 1:8),
#' as.data.frame(exampleDat)))
#' names(exampleDat) <- c('ID', itemNames)
#'
#' ## Returns data frame with scale scores and with original items untouched
#' scoredDat <- scoreFACT_ES_BCS(exampleDat)
#' names(scoredDat)
#' scoredDat
#' ## Returns data frame with scale scores, with the appropriate items
#' ## reverse scored, and with item values of 8 and 9 replaced with NA.
#' ## Also illustrates the effect of setting keepNvalid = TRUE.
#' scoredDat <- scoreFACT_ES_BCS(exampleDat, updateItems = TRUE, keepNvalid = TRUE)
#' names(scoredDat)
#' ## Descriptives of scored scales
#' summary(scoredDat[, c('PWB', 'SWB', 'EWB', 'FWB', 'FACTG',
#' 'ESS_19', 'ESS_23', 'BCS', 'FACT_ES_TOTAL', 'FACT_B_TOI')])
scoreFACT_ES_BCS <- function(df, updateItems = FALSE, keepNvalid = FALSE) {
dfG <- scoreFACTG(df, updateItems = updateItems, keepNvalid = TRUE)
dfGup <- dfG
names(dfGup) <- toupper(names(dfGup))
names(df) <- toupper(names(df))
G_names <- c(paste0("GP", 1:7), paste0("GS", 1:7), paste0("GE", 1:6),
paste0("GF", 1:7))
AC_names1 <- toupper(c("ES1", "ES2", "ES3", "ES4", "ES5", "ES6", "ES7",
"ES8", "ES9", "ES10", "An9", "O2", "C5", "An10", "Tax1", "ES11",
"ES12", "ES13", "BRM1"))
AC_names2 <- toupper(c("ES1", "ES2", "ES3", "ES4", "ES5", "ES6", "ES7",
"ES8", "ES9", "ES10", "An9", "O2", "C5", "An10", "Tax1", "ES11",
"ES12", "ES13", "BRM1", "GP1", "GP2", "GE4", "GF5"))
AC_names3 <- toupper(c("B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8",
"B9"))
AC_names <- unique(c(AC_names1, AC_names2, AC_names3))
revNames <- unique(toupper(c("B1", "B2", "B3", "B5", "B6", "B7", "B8",
"ES1", "ES2", "ES3", "ES4", "ES5", "ES6", "ES7", "ES8", "ES9",
"ES10", "An9", "O2", "C5", "An10", "Tax1", "ES11", "ES12", "ES13",
"BRM1", "ES1", "ES2", "ES3", "ES4", "ES5", "ES6", "ES7", "ES8",
"ES9", "ES10", "An9", "O2", "C5", "An10", "Tax1", "ES11", "ES12",
"ES13", "BRM1", "GP1", "GP2", "GE4")))
AC_items <- df[, AC_names]
if (any(!(as.matrix(AC_items) %in% c(0:4, 8, 9, NA)))) {
stop("At least 1 response is out of range (i.e., not 0-4, 8, 9, or NA)")
}
makeMiss <- function(x) {
x[x %in% c(8, 9)] <- NA
return(x)
}
AC_items <- as.data.frame(lapply(AC_items, makeMiss))
revHelper <- function(x) {
return(4 - x)
}
AC_items[, revNames] <- lapply(AC_items[, revNames], revHelper)
valid_N <- as.data.frame(lapply(AC_items, function(x) as.numeric(!is.na(x))))
AC_N1 <- rowSums(valid_N[, AC_names1])
AC_N2 <- rowSums(valid_N[, AC_names2])
AC_N3 <- rowSums(valid_N[, AC_names3])
FACT_B_TOTAL_N <- dfG$PWB_N + dfG$SWB_N + dfG$EWB_N + dfG$FWB_N + AC_N3
FACT_ES_TOTAL_N <- dfG$PWB_N + dfG$SWB_N + dfG$EWB_N + dfG$FWB_N +
AC_N1 + AC_N3
AC1 <- round(rowMeans(AC_items[, AC_names1], na.rm = TRUE) * length(AC_names1),
3)
AC1[AC_N1/length(AC_names1) <= 0.5] <- NA
AC2 <- round(rowMeans(AC_items[, AC_names2], na.rm = TRUE) * length(AC_names2),
3)
AC2[AC_N2/length(AC_names2) <= 0.5] <- NA
AC3 <- round(rowMeans(AC_items[, AC_names3], na.rm = TRUE) * length(AC_names3),
3)
AC3[AC_N3/length(AC_names3) <= 0.5] <- NA
FACT_B_TOTAL <- dfG$PWB + dfG$SWB + dfG$EWB + dfG$FWB + AC3
FACT_B_TOTAL[FACT_B_TOTAL_N/length(unique(c(G_names, AC_names3))) <=
0.8] <- NA
FACT_B_TOI <- dfG$PWB + dfG$FWB + AC3
FACT_ES_TOTAL <- dfG$PWB + dfG$SWB + dfG$EWB + dfG$FWB + AC1 + AC3
FACT_ES_TOTAL[FACT_ES_TOTAL_N/length(unique(c(G_names, AC_names1, AC_names3))) <=
0.8] <- NA
ESS_19_N <- AC_N1
ESS_23_N <- AC_N2
BCS_N <- AC_N3
ESS_19 <- AC1
ESS_23 <- AC2
BCS <- AC3
if (updateItems) {
dfItemPos <- unlist(sapply(AC_names, function(x) grep(x, names(df),
ignore.case = TRUE, value = FALSE)))
names(df)[dfItemPos] <- toupper(names(df)[dfItemPos])
df[, AC_names] <- AC_items
}
if (keepNvalid) {
dfOut <- as.data.frame(cbind(dfG, ESS_19_N, ESS_23_N, BCS_N, FACT_ES_TOTAL_N,
FACT_B_TOTAL_N, ESS_19, ESS_23, BCS, FACT_B_TOTAL, FACT_ES_TOTAL,
FACT_B_TOI))
} else {
dfG[, "PWB_N"] <- NULL
dfG[, "SWB_N"] <- NULL
dfG[, "EWB_N"] <- NULL
dfG[, "FWB_N"] <- NULL
dfG[, "FACTG_N"] <- NULL
dfOut <- as.data.frame(cbind(dfG, ESS_19, ESS_23, BCS, FACT_B_TOTAL,
FACT_ES_TOTAL, FACT_B_TOI))
}
return(dfOut)
}
raybaser/FACTscorer documentation built on March 29, 2022, 7:50 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions scoreFACT_ES_BCS
Documented in scoreFACT_ES_BCS
#' @title Score the FACT-ES-BCS
#'
#' @description
#' Generates all of the scores of the Functional Assessment of Cancer Therapy -
#' Endocrine Symptom with Breast Cancer Subscale (FACT-ES-BCS, v4)
#' from item responses.
#'
#' @details
#' Given a data frame that includes all of the FACT-ES-BCS (Version 4) items as
#' variables, appropriately named, this function generates all of the FACT-ES
#' scale scores. It is crucial that the item variables in the supplied data
#' frame are named according to FACT conventions. For example, the first
#' physical well-being item should be named GP1, the second GP2, and so on.
#' Please refer to the materials provided by \url{http://www.facit.org} for the
#' particular questionnaire you are using. In particular, refer to the left
#' margin of the official questionnaire (i.e., from facit.org) for the
#' appropriate item variable names.
#'
#' @section Note:
#' Keep in mind that this function (and R in general) is case-sensitive.
#'
#' All variables should be in numeric or integer format.
#'
#' This scoring function expects missing item responses to be coded as NA,
#' 8, or 9, and valid item responses to be coded as 0, 1, 2, 3, or 4. Any
#' other value for any of the items will result in an error message and no
#' scores.
#'
#' Some item variables are reverse coded for the purpose of generating the
#' scale scores. The official (e.g., from \url{http://www.facit.org}) SAS
#' and SPSS scoring algorithms for this questionnaire automatically replace
#' the original items with their reverse-coded versions. This can be
#' confusing if you accidentally run the algorithm more than once on your
#' data. As its default, \code{scoreFACT_ES} DOES NOT replace any of your
#' original item variables with the reverse coded versions. However, for
#' consistentcy with the behavior of the other versions on
#' \url{http://www.facit.org}, the \code{updateItems} argument is
#' provided. If set to \code{TRUE}, any item that is supposed to be
#' reverse coded will be replaced with its reversed version in the data
#' frame returned by \code{scoreFACT_ES}.
#'
#'
#' @param df A data frame with the FACT-ES items, appropriately-named.
#' @param updateItems Logical, if \code{TRUE} any original item that is
#' reverse coded for scoring will be replaced by its reverse coded version
#' in the returned data frame, and any values of 8 or 9 will be replaced
#' with NA. The default, \code{FALSE}, returns the original items
#' unmodified.
#' @param keepNvalid Logical, if \code{TRUE} the function
#' returns an additional variable for each of the returned scale scores
#' containing the number of valid, non-missing responses from each
#' respondent to the items on the given scale. If \code{FALSE} (the
#' default), these variables are omitted from the returned data frame.
#'
#'
#' @return The original data frame is returned (optionally with modified
#' items if \code{updateItems = TRUE}) with new variables corresponding to
#' the scored scales. If \code{keepNvalid = TRUE}, for each scored scale an
#' additional variable is returned that contains the number of valid
#' responses each respondent made to the items making up the given scale.
#' These optional variables have names of the format \code{SCALENAME_N}.
#' The following scale scores are returned:
#'
#' \describe{
#' \item{PWB}{Physical Well-Being subscale}
#' \item{SWB}{Social/Family Well-Being subscale}
#' \item{EWB}{Emotional Well-Being subscale}
#' \item{FWB}{Physical Well-Being subscale}
#' \item{FACTG}{FACT-G Total Score (i.e., PWB+SWB+EWB+FWB)}
#' \item{ESS_19}{19-item Endocrine Symptom subscale}
#' \item{ESS_23}{23-item Endocrine Symptom subscale}
#' \item{BCS}{Breast Cancer subscale}
#' \item{FACT_ES_TOTAL}{FACT-ES Total Score (i.e., PWB+SWB+EWB+FWB+ESS_19+ESS_23+BCS)}
#' \item{FACT_ES_TOI}{FACT-ES Trial Outcome Index (e.g., PWB+FWB+ESS_19+ESS_23+BCS)}
#' }
#'
#' @references FACT-ES Scoring Guidelines, available at \url{http://www.facit.org}
#'
#' @export
#'
#' @examples
#' ## Setting up item names for fake data
#' G_names <- c(paste0('GP', 1:7),
#' paste0('GS', 1:7),
#' paste0('GE', 1:6),
#' paste0('GF', 1:7))
#' AC_names1 <- c('ES1', 'ES2', 'ES3', 'ES4', 'ES5', 'ES6', 'ES7', 'ES8', 'ES9', 'ES10',
#' 'An9', 'O2', 'C5', 'An10', 'Tax1', 'ES11', 'ES12', 'ES13', 'BRM1')
#' AC_names2 <- c('ES1', 'ES2', 'ES3', 'ES4', 'ES5', 'ES6', 'ES7', 'ES8', 'ES9', 'ES10',
#' 'An9', 'O2', 'C5', 'An10', 'Tax1', 'ES11', 'ES12', 'ES13', 'BRM1',
#' 'GP1', 'GP2', 'GE4', 'GF5')
#' AC_names3 <- c('B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B9')
#' AC_names <- c(AC_names1, AC_names2, AC_names3)
#' itemNames <- c(G_names, AC_names)
#' ## Generating random item responses for 8 fake respondents
#' set.seed(6375309)
#' exampleDat <- t(replicate(8, sample(0:4, size = length(itemNames), replace = TRUE)))
#' ## Making half of respondents missing about 10% of items,
#' ## half missing about 50%.
#' miss10 <- t(replicate(4, sample(c(0, 9), prob = c(0.9, 0.1),
#' size = length(itemNames), replace = TRUE)))
#' miss50 <- t(replicate(4, sample(c(0, 9), prob = c(0.5, 0.5),
#' size = length(itemNames), replace = TRUE)))
#' missMtx <- rbind(miss10, miss50)
#' ## Using 9 as the code for missing responses
#' exampleDat[missMtx == 9] <- 9
#' exampleDat <- as.data.frame(cbind(ID = paste0('ID', 1:8),
#' as.data.frame(exampleDat)))
#' names(exampleDat) <- c('ID', itemNames)
#'
#' ## Returns data frame with scale scores and with original items untouched
#' scoredDat <- scoreFACT_ES_BCS(exampleDat)
#' names(scoredDat)
#' scoredDat
#' ## Returns data frame with scale scores, with the appropriate items
#' ## reverse scored, and with item values of 8 and 9 replaced with NA.
#' ## Also illustrates the effect of setting keepNvalid = TRUE.
#' scoredDat <- scoreFACT_ES_BCS(exampleDat, updateItems = TRUE, keepNvalid = TRUE)
#' names(scoredDat)
#' ## Descriptives of scored scales
#' summary(scoredDat[, c('PWB', 'SWB', 'EWB', 'FWB', 'FACTG',
#' 'ESS_19', 'ESS_23', 'BCS', 'FACT_ES_TOTAL', 'FACT_B_TOI')])
scoreFACT_ES_BCS <- function(df, updateItems = FALSE, keepNvalid = FALSE) {
dfG <- scoreFACTG(df, updateItems = updateItems, keepNvalid = TRUE)
dfGup <- dfG
names(dfGup) <- toupper(names(dfGup))
names(df) <- toupper(names(df))
G_names <- c(paste0("GP", 1:7), paste0("GS", 1:7), paste0("GE", 1:6),
paste0("GF", 1:7))
AC_names1 <- toupper(c("ES1", "ES2", "ES3", "ES4", "ES5", "ES6", "ES7",
"ES8", "ES9", "ES10", "An9", "O2", "C5", "An10", "Tax1", "ES11",
"ES12", "ES13", "BRM1"))
AC_names2 <- toupper(c("ES1", "ES2", "ES3", "ES4", "ES5", "ES6", "ES7",
"ES8", "ES9", "ES10", "An9", "O2", "C5", "An10", "Tax1", "ES11",
"ES12", "ES13", "BRM1", "GP1", "GP2", "GE4", "GF5"))
AC_names3 <- toupper(c("B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8",
"B9"))
AC_names <- unique(c(AC_names1, AC_names2, AC_names3))
revNames <- unique(toupper(c("B1", "B2", "B3", "B5", "B6", "B7", "B8",
"ES1", "ES2", "ES3", "ES4", "ES5", "ES6", "ES7", "ES8", "ES9",
"ES10", "An9", "O2", "C5", "An10", "Tax1", "ES11", "ES12", "ES13",
"BRM1", "ES1", "ES2", "ES3", "ES4", "ES5", "ES6", "ES7", "ES8",
"ES9", "ES10", "An9", "O2", "C5", "An10", "Tax1", "ES11", "ES12",
"ES13", "BRM1", "GP1", "GP2", "GE4")))
AC_items <- df[, AC_names]
if (any(!(as.matrix(AC_items) %in% c(0:4, 8, 9, NA)))) {
stop("At least 1 response is out of range (i.e., not 0-4, 8, 9, or NA)")
}
makeMiss <- function(x) {
x[x %in% c(8, 9)] <- NA
return(x)
}
AC_items <- as.data.frame(lapply(AC_items, makeMiss))
revHelper <- function(x) {
return(4 - x)
}
AC_items[, revNames] <- lapply(AC_items[, revNames], revHelper)
valid_N <- as.data.frame(lapply(AC_items, function(x) as.numeric(!is.na(x))))
AC_N1 <- rowSums(valid_N[, AC_names1])
AC_N2 <- rowSums(valid_N[, AC_names2])
AC_N3 <- rowSums(valid_N[, AC_names3])
FACT_B_TOTAL_N <- dfG$PWB_N + dfG$SWB_N + dfG$EWB_N + dfG$FWB_N + AC_N3
FACT_ES_TOTAL_N <- dfG$PWB_N + dfG$SWB_N + dfG$EWB_N + dfG$FWB_N +
AC_N1 + AC_N3
AC1 <- round(rowMeans(AC_items[, AC_names1], na.rm = TRUE) * length(AC_names1),
3)
AC1[AC_N1/length(AC_names1) <= 0.5] <- NA
AC2 <- round(rowMeans(AC_items[, AC_names2], na.rm = TRUE) * length(AC_names2),
3)
AC2[AC_N2/length(AC_names2) <= 0.5] <- NA
AC3 <- round(rowMeans(AC_items[, AC_names3], na.rm = TRUE) * length(AC_names3),
3)
AC3[AC_N3/length(AC_names3) <= 0.5] <- NA
FACT_B_TOTAL <- dfG$PWB + dfG$SWB + dfG$EWB + dfG$FWB + AC3
FACT_B_TOTAL[FACT_B_TOTAL_N/length(unique(c(G_names, AC_names3))) <=
0.8] <- NA
FACT_B_TOI <- dfG$PWB + dfG$FWB + AC3
FACT_ES_TOTAL <- dfG$PWB + dfG$SWB + dfG$EWB + dfG$FWB + AC1 + AC3
FACT_ES_TOTAL[FACT_ES_TOTAL_N/length(unique(c(G_names, AC_names1, AC_names3))) <=
0.8] <- NA
ESS_19_N <- AC_N1
ESS_23_N <- AC_N2
BCS_N <- AC_N3
ESS_19 <- AC1
ESS_23 <- AC2
BCS <- AC3
if (updateItems) {
dfItemPos <- unlist(sapply(AC_names, function(x) grep(x, names(df),
ignore.case = TRUE, value = FALSE)))
names(df)[dfItemPos] <- toupper(names(df)[dfItemPos])
df[, AC_names] <- AC_items
}
if (keepNvalid) {
dfOut <- as.data.frame(cbind(dfG, ESS_19_N, ESS_23_N, BCS_N, FACT_ES_TOTAL_N,
FACT_B_TOTAL_N, ESS_19, ESS_23, BCS, FACT_B_TOTAL, FACT_ES_TOTAL,
FACT_B_TOI))
} else {
dfG[, "PWB_N"] <- NULL
dfG[, "SWB_N"] <- NULL
dfG[, "EWB_N"] <- NULL
dfG[, "FWB_N"] <- NULL
dfG[, "FACTG_N"] <- NULL
dfOut <- as.data.frame(cbind(dfG, ESS_19, ESS_23, BCS, FACT_B_TOTAL,
FACT_ES_TOTAL, FACT_B_TOI))
}
return(dfOut)
}
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