Description Usage Arguments Details Value Note References Examples
View source: R/ca-scoreFACT_Bl.R
Generates all of the scores of the Functional Assessment of Cancer Therapy - Bladder Cancer (FACT-Bl, v4) from item responses.
1 | scoreFACT_Bl(df, updateItems = FALSE, keepNvalid = FALSE)
|
df |
A data frame with the FACT-Bl items, appropriately-named. |
updateItems |
Logical, if |
keepNvalid |
Logical, if |
Given a data frame that includes all of the FACT-Bl (Version 4) items as variables, appropriately named, this function generates all of the FACT-Bl 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 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.
The original data frame is returned (optionally with modified
items if updateItems = TRUE
) with new variables corresponding to
the scored scales. If 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 SCALENAME_N
.
The following scale scores are returned:
Physical Well-Being subscale
Social/Family Well-Being subscale
Emotional Well-Being subscale
Physical Well-Being subscale
FACT-G Total Score (i.e., PWB+SWB+EWB+FWB)
Bladder Cancer subscale
FACT-Bl Total Score (i.e., PWB+SWB+EWB+FWB+BlCS)
FACT-Bl Trial Outcome Index (e.g., PWB+FWB+BlCS)
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 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, scoreFACT_Bl
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
http://www.facit.org, the updateItems
argument is
provided. If set to TRUE
, any item that is supposed to be
reverse coded will be replaced with its reversed version in the data
frame returned by scoreFACT_Bl
.
FACT-Bl Scoring Guidelines, available at http://www.facit.org
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | ## 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_names <- c('BL1', 'C2', 'C3', 'BL2', 'C5', 'C6', 'C7', 'BL3', 'BL4', 'BL5', 'C8', 'C9')
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_Bl(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_Bl(exampleDat, updateItems = TRUE, keepNvalid = TRUE)
names(scoredDat)
## Descriptives of scored scales
summary(scoredDat[, c('PWB', 'SWB', 'EWB', 'FWB', 'FACTG',
'BlCS', 'FACT_Bl_TOTAL', 'FACT_Bl_TOI')])
|
[1] "ID" "GP1" "GP2" "GP3"
[5] "GP4" "GP5" "GP6" "GP7"
[9] "GS1" "GS2" "GS3" "GS4"
[13] "GS5" "GS6" "GS7" "GE1"
[17] "GE2" "GE3" "GE4" "GE5"
[21] "GE6" "GF1" "GF2" "GF3"
[25] "GF4" "GF5" "GF6" "GF7"
[29] "BL1" "C2" "C3" "BL2"
[33] "C5" "C6" "C7" "BL3"
[37] "BL4" "BL5" "C8" "C9"
[41] "PWB" "SWB" "EWB" "FWB"
[45] "FACTG" "BlCS" "FACT_Bl_TOTAL" "FACT_Bl_TOI"
ID GP1 GP2 GP3 GP4 GP5 GP6 GP7 GS1 GS2 GS3 GS4 GS5 GS6 GS7 GE1 GE2 GE3 GE4
1 ID1 3 3 1 9 2 3 0 1 2 1 1 1 0 2 0 2 0 2
2 ID2 3 2 4 2 2 3 1 2 1 1 0 3 4 9 9 3 3 3
3 ID3 4 0 9 2 2 1 0 0 3 3 2 9 2 1 0 4 3 3
4 ID4 3 1 1 1 4 1 9 0 2 1 2 2 2 3 2 4 2 1
5 ID5 9 9 9 2 3 9 9 9 9 0 0 9 9 9 9 9 0 4
6 ID6 9 0 9 9 0 3 1 4 9 0 9 3 1 9 0 9 9 2
7 ID7 9 0 9 9 2 9 1 3 9 9 1 9 4 9 9 1 9 9
8 ID8 0 0 2 9 4 9 3 0 9 9 0 4 0 3 4 9 9 3
GE5 GE6 GF1 GF2 GF3 GF4 GF5 GF6 GF7 BL1 C2 C3 BL2 C5 C6 C7 BL3 BL4 BL5 C8 C9
1 4 3 0 3 9 0 1 2 2 4 2 1 2 3 9 4 4 4 3 0 4
2 2 0 2 2 3 3 4 4 0 0 3 3 3 2 3 3 2 3 0 0 2
3 9 2 1 1 9 3 4 4 0 1 1 2 0 4 0 3 2 3 2 4 3
4 9 4 3 4 2 0 1 0 2 1 1 0 0 1 1 1 1 2 3 2 3
5 4 9 9 2 4 0 1 9 9 1 2 9 0 9 0 2 3 9 0 9 9
6 1 0 9 9 9 9 9 9 9 2 9 2 9 0 1 9 9 9 2 2 9
7 9 9 4 9 1 3 1 9 3 1 9 9 9 9 0 0 9 4 9 1 2
8 3 9 9 0 9 4 9 0 9 9 1 9 9 9 0 1 9 4 4 9 4
PWB SWB EWB FWB FACTG BlCS FACT_Bl_TOTAL FACT_Bl_TOI
1 14.000 8.000 13.0 9.333 44.333 22.909 67.242 46.242
2 11.000 12.833 13.2 18.000 55.033 28.000 83.033 57.000
3 17.500 12.833 14.4 15.167 59.900 23.000 82.900 55.667
4 15.167 12.000 13.2 12.000 52.367 26.000 78.367 53.167
5 NA NA NA 12.250 NA 20.571 NA NA
6 21.000 14.000 19.5 NA NA NA NA NA
7 NA NA NA 16.800 NA NA NA NA
8 15.400 9.800 NA NA NA NA NA NA
[1] "ID" "GP1" "GP2" "GP3"
[5] "GP4" "GP5" "GP6" "GP7"
[9] "GS1" "GS2" "GS3" "GS4"
[13] "GS5" "GS6" "GS7" "GE1"
[17] "GE2" "GE3" "GE4" "GE5"
[21] "GE6" "GF1" "GF2" "GF3"
[25] "GF4" "GF5" "GF6" "GF7"
[29] "BL1" "C2" "C3" "BL2"
[33] "C5" "C6" "C7" "BL3"
[37] "BL4" "BL5" "C8" "C9"
[41] "PWB_N" "SWB_N" "EWB_N" "FWB_N"
[45] "FACTG_N" "PWB" "SWB" "EWB"
[49] "FWB" "FACTG" "BlCS_N" "FACT_Bl_TOTAL_N"
[53] "BlCS" "FACT_Bl_TOTAL" "FACT_Bl_TOI"
PWB SWB EWB FWB
Min. :11.00 Min. : 8.00 Min. :13.00 Min. : 9.333
1st Qu.:14.29 1st Qu.:10.35 1st Qu.:13.20 1st Qu.:12.062
Median :15.28 Median :12.42 Median :13.20 Median :13.709
Mean :15.68 Mean :11.58 Mean :14.66 Mean :13.925
3rd Qu.:16.98 3rd Qu.:12.83 3rd Qu.:14.40 3rd Qu.:16.392
Max. :21.00 Max. :14.00 Max. :19.50 Max. :18.000
NA's :2 NA's :2 NA's :3 NA's :2
FACTG BlCS FACT_Bl_TOTAL FACT_Bl_TOI
Min. :44.33 Min. :20.57 Min. :67.24 Min. :46.24
1st Qu.:50.36 1st Qu.:22.91 1st Qu.:75.59 1st Qu.:51.44
Median :53.70 Median :23.00 Median :80.63 Median :54.42
Mean :52.91 Mean :24.10 Mean :77.89 Mean :53.02
3rd Qu.:56.25 3rd Qu.:26.00 3rd Qu.:82.93 3rd Qu.:56.00
Max. :59.90 Max. :28.00 Max. :83.03 Max. :57.00
NA's :4 NA's :3 NA's :4 NA's :4
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