scoreFACT_BL_CYS: Score the FACT-BL-CYS
In FACTscorer: Scores the FACT and FACIT Family of Patient-Reported Outcome Measures
Description
Usage
Arguments
Details
Value
Note
References
Examples
View source: R/tx-scoreFACT_BL_CYS.R
Description
Generates all of the scores of the Functional Assessment of Cancer Therapy -
BL-CYS (FACT-BL-CYS, v4) from item responses.
Usage
1
scoreFACT_BL_CYS(df, updateItems = FALSE, keepNvalid = FALSE)
Arguments
df
A data frame with the FACT-BL-CYS items, appropriately-named.
updateItems
Logical, if 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, FALSE, returns the original items
unmodified.
keepNvalid
Logical, if 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 FALSE (the
default), these variables are omitted from the returned data frame.
Details
Given a data frame that includes all of the FACT-BL-CYS (Version 4) items as
variables, appropriately named, this function generates all of the FACT-BL-CYS
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.
Value
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:
- PWB
Physical Well-Being subscale
- SWB
Social/Family Well-Being subscale
- EWB
Emotional Well-Being subscale
- FWB
Physical Well-Being subscale
- FACTG
FACT-G Total Score (i.e., PWB+SWB+EWB+FWB)
- BL_CYS
BL-CYS subscale
- FACT_BL_CYS_TOTAL
FACT-BL-CYS Total Score (i.e., PWB+SWB+EWB+FWB+BL_CYS)
- FACT_BL_CYS_TOI
FACT-BL-CYS Trial Outcome Index (e.g., PWB+FWB+BL_CYS)
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 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-CYS 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-CYS.
References
FACT-BL-CYS Scoring Guidelines, available at http://www.facit.org
Examples
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
36
## 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('C2', 'C3', 'C4', 'C6', 'C7', 'BL1', 'ITU7',
'ITU6', 'C9', 'ITU3', 'ITU4', 'ITU5', 'ITU1', 'VC1', 'ITU2')
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_CYS(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_CYS(exampleDat, updateItems = TRUE, keepNvalid = TRUE)
names(scoredDat)
## Descriptives of scored scales
summary(scoredDat[, c('PWB', 'SWB', 'EWB', 'FWB', 'FACTG',
'BL_CYS', 'FACT_BL_CYS_TOTAL', 'FACT_BL_CYS_TOI')])
Example output
[1] "ID" "GP1" "GP2"
[4] "GP3" "GP4" "GP5"
[7] "GP6" "GP7" "GS1"
[10] "GS2" "GS3" "GS4"
[13] "GS5" "GS6" "GS7"
[16] "GE1" "GE2" "GE3"
[19] "GE4" "GE5" "GE6"
[22] "GF1" "GF2" "GF3"
[25] "GF4" "GF5" "GF6"
[28] "GF7" "C2" "C3"
[31] "C4" "C6" "C7"
[34] "BL1" "ITU7" "ITU6"
[37] "C9" "ITU3" "ITU4"
[40] "ITU5" "ITU1" "VC1"
[43] "ITU2" "PWB" "SWB"
[46] "EWB" "FWB" "FACTG"
[49] "BL_CYS" "FACT_BL_CYS_TOTAL" "FACT_BL_CYS_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 2 2 3 0 1 9 1 1 1 0 2 0 2 0 2
2 ID2 2 2 3 1 2 1 1 0 3 4 0 0 3 3 9 2 0 2
3 ID3 0 0 3 3 2 2 2 1 0 4 3 3 0 2 1 9 2 3
4 ID4 1 2 2 2 3 2 4 2 1 1 4 3 4 2 0 1 0 2
5 ID5 1 9 9 9 0 9 9 4 1 2 4 9 1 9 3 1 9 0
6 ID6 9 1 9 9 0 9 2 2 9 9 3 9 2 9 9 4 9 9
7 ID7 9 4 9 4 1 9 9 1 3 1 1 4 9 9 0 0 9 9
8 ID8 9 4 4 9 0 9 9 1 2 2 9 0 9 9 9 9 9 9
GE5 GE6 GF1 GF2 GF3 GF4 GF5 GF6 GF7 C2 C3 C4 C6 C7 BL1 ITU7 ITU6 C9 ITU3 ITU4
1 4 3 0 3 2 0 1 2 2 4 9 9 2 3 0 4 4 4 3 0
2 2 3 3 4 4 9 0 3 3 3 2 3 9 2 3 0 9 2 4 0
3 4 4 0 1 1 2 0 4 0 9 2 3 2 4 3 3 1 1 1 4
4 1 1 0 0 1 1 9 1 2 3 2 9 2 0 0 2 3 2 3 0
5 9 9 0 2 3 9 9 9 9 9 9 9 2 9 3 9 4 2 9 9
6 9 9 1 9 2 2 1 9 3 2 9 9 9 9 2 0 9 3 9 4
7 4 9 2 9 0 9 9 4 9 9 9 4 0 9 4 0 9 4 2 9
8 2 9 1 9 9 2 0 3 9 9 9 3 0 9 0 9 2 0 9 2
ITU5 ITU1 VC1 ITU2 PWB SWB EWB FWB FACTG BL_CYS FACT_BL_CYS_TOTAL
1 4 3 2 4 14 7.00 13.0 10.000 44.000 21.923 65.923
2 2 2 2 1 16 13.00 13.2 19.833 62.033 32.308 94.341
3 1 2 0 2 16 13.00 7.2 8.000 44.200 31.071 75.271
4 9 0 9 0 12 17.00 17.0 5.833 51.833 28.750 80.583
5 9 1 1 9 NA 16.80 NA NA NA NA NA
6 0 1 4 1 NA NA NA 12.600 NA 35.000 NA
7 3 9 0 4 NA 14.00 NA NA NA 18.333 NA
8 2 0 9 9 NA 8.75 NA 10.500 NA 31.875 NA
FACT_BL_CYS_TOI
1 45.923
2 68.141
3 55.071
4 46.583
5 NA
6 NA
7 NA
8 NA
[1] "ID" "GP1" "GP2"
[4] "GP3" "GP4" "GP5"
[7] "GP6" "GP7" "GS1"
[10] "GS2" "GS3" "GS4"
[13] "GS5" "GS6" "GS7"
[16] "GE1" "GE2" "GE3"
[19] "GE4" "GE5" "GE6"
[22] "GF1" "GF2" "GF3"
[25] "GF4" "GF5" "GF6"
[28] "GF7" "C2" "C3"
[31] "C4" "C6" "C7"
[34] "BL1" "ITU7" "ITU6"
[37] "C9" "ITU3" "ITU4"
[40] "ITU5" "ITU1" "VC1"
[43] "ITU2" "PWB_N" "SWB_N"
[46] "EWB_N" "FWB_N" "FACTG_N"
[49] "PWB" "SWB" "EWB"
[52] "FWB" "FACTG" "BL_CYS_N"
[55] "FACT_BL_CYS_TOTAL_N" "BL_CYS" "FACT_BL_CYS_TOTAL"
[58] "FACT_BL_CYS_TOI"
PWB SWB EWB FWB
Min. :12.0 Min. : 7.00 Min. : 7.20 Min. : 5.833
1st Qu.:13.5 1st Qu.:10.88 1st Qu.:11.55 1st Qu.: 8.500
Median :15.0 Median :13.00 Median :13.10 Median :10.250
Mean :14.5 Mean :12.79 Mean :12.60 Mean :11.128
3rd Qu.:16.0 3rd Qu.:15.40 3rd Qu.:14.15 3rd Qu.:12.075
Max. :16.0 Max. :17.00 Max. :17.00 Max. :19.833
NA's :4 NA's :1 NA's :4 NA's :2
FACTG BL_CYS FACT_BL_CYS_TOTAL FACT_BL_CYS_TOI
Min. :44.00 Min. :18.33 Min. :65.92 Min. :45.92
1st Qu.:44.15 1st Qu.:25.34 1st Qu.:72.93 1st Qu.:46.42
Median :48.02 Median :31.07 Median :77.93 Median :50.83
Mean :50.52 Mean :28.47 Mean :79.03 Mean :53.93
3rd Qu.:54.38 3rd Qu.:32.09 3rd Qu.:84.02 3rd Qu.:58.34
Max. :62.03 Max. :35.00 Max. :94.34 Max. :68.14
NA's :4 NA's :1 NA's :4 NA's :4
FACTscorer documentation built on May 29, 2017, 3:45 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value Note References Examples
View source: R/tx-scoreFACT_BL_CYS.R
Description
Generates all of the scores of the Functional Assessment of Cancer Therapy - BL-CYS (FACT-BL-CYS, v4) from item responses.
Usage
1 | scoreFACT_BL_CYS(df, updateItems = FALSE, keepNvalid = FALSE)
|
Arguments
df |
A data frame with the FACT-BL-CYS items, appropriately-named. |
updateItems |
Logical, if |
keepNvalid |
Logical, if |
Details
Given a data frame that includes all of the FACT-BL-CYS (Version 4) items as variables, appropriately named, this function generates all of the FACT-BL-CYS 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.
Value
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:
- PWB
Physical Well-Being subscale
- SWB
Social/Family Well-Being subscale
- EWB
Emotional Well-Being subscale
- FWB
Physical Well-Being subscale
- FACTG
FACT-G Total Score (i.e., PWB+SWB+EWB+FWB)
- BL_CYS
BL-CYS subscale
- FACT_BL_CYS_TOTAL
FACT-BL-CYS Total Score (i.e., PWB+SWB+EWB+FWB+BL_CYS)
- FACT_BL_CYS_TOI
FACT-BL-CYS Trial Outcome Index (e.g., PWB+FWB+BL_CYS)
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 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-CYS 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-CYS.
References
FACT-BL-CYS Scoring Guidelines, available at http://www.facit.org
Examples
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 36 | ## 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('C2', 'C3', 'C4', 'C6', 'C7', 'BL1', 'ITU7',
'ITU6', 'C9', 'ITU3', 'ITU4', 'ITU5', 'ITU1', 'VC1', 'ITU2')
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_CYS(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_CYS(exampleDat, updateItems = TRUE, keepNvalid = TRUE)
names(scoredDat)
## Descriptives of scored scales
summary(scoredDat[, c('PWB', 'SWB', 'EWB', 'FWB', 'FACTG',
'BL_CYS', 'FACT_BL_CYS_TOTAL', 'FACT_BL_CYS_TOI')])
|
Example output
[1] "ID" "GP1" "GP2"
[4] "GP3" "GP4" "GP5"
[7] "GP6" "GP7" "GS1"
[10] "GS2" "GS3" "GS4"
[13] "GS5" "GS6" "GS7"
[16] "GE1" "GE2" "GE3"
[19] "GE4" "GE5" "GE6"
[22] "GF1" "GF2" "GF3"
[25] "GF4" "GF5" "GF6"
[28] "GF7" "C2" "C3"
[31] "C4" "C6" "C7"
[34] "BL1" "ITU7" "ITU6"
[37] "C9" "ITU3" "ITU4"
[40] "ITU5" "ITU1" "VC1"
[43] "ITU2" "PWB" "SWB"
[46] "EWB" "FWB" "FACTG"
[49] "BL_CYS" "FACT_BL_CYS_TOTAL" "FACT_BL_CYS_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 2 2 3 0 1 9 1 1 1 0 2 0 2 0 2
2 ID2 2 2 3 1 2 1 1 0 3 4 0 0 3 3 9 2 0 2
3 ID3 0 0 3 3 2 2 2 1 0 4 3 3 0 2 1 9 2 3
4 ID4 1 2 2 2 3 2 4 2 1 1 4 3 4 2 0 1 0 2
5 ID5 1 9 9 9 0 9 9 4 1 2 4 9 1 9 3 1 9 0
6 ID6 9 1 9 9 0 9 2 2 9 9 3 9 2 9 9 4 9 9
7 ID7 9 4 9 4 1 9 9 1 3 1 1 4 9 9 0 0 9 9
8 ID8 9 4 4 9 0 9 9 1 2 2 9 0 9 9 9 9 9 9
GE5 GE6 GF1 GF2 GF3 GF4 GF5 GF6 GF7 C2 C3 C4 C6 C7 BL1 ITU7 ITU6 C9 ITU3 ITU4
1 4 3 0 3 2 0 1 2 2 4 9 9 2 3 0 4 4 4 3 0
2 2 3 3 4 4 9 0 3 3 3 2 3 9 2 3 0 9 2 4 0
3 4 4 0 1 1 2 0 4 0 9 2 3 2 4 3 3 1 1 1 4
4 1 1 0 0 1 1 9 1 2 3 2 9 2 0 0 2 3 2 3 0
5 9 9 0 2 3 9 9 9 9 9 9 9 2 9 3 9 4 2 9 9
6 9 9 1 9 2 2 1 9 3 2 9 9 9 9 2 0 9 3 9 4
7 4 9 2 9 0 9 9 4 9 9 9 4 0 9 4 0 9 4 2 9
8 2 9 1 9 9 2 0 3 9 9 9 3 0 9 0 9 2 0 9 2
ITU5 ITU1 VC1 ITU2 PWB SWB EWB FWB FACTG BL_CYS FACT_BL_CYS_TOTAL
1 4 3 2 4 14 7.00 13.0 10.000 44.000 21.923 65.923
2 2 2 2 1 16 13.00 13.2 19.833 62.033 32.308 94.341
3 1 2 0 2 16 13.00 7.2 8.000 44.200 31.071 75.271
4 9 0 9 0 12 17.00 17.0 5.833 51.833 28.750 80.583
5 9 1 1 9 NA 16.80 NA NA NA NA NA
6 0 1 4 1 NA NA NA 12.600 NA 35.000 NA
7 3 9 0 4 NA 14.00 NA NA NA 18.333 NA
8 2 0 9 9 NA 8.75 NA 10.500 NA 31.875 NA
FACT_BL_CYS_TOI
1 45.923
2 68.141
3 55.071
4 46.583
5 NA
6 NA
7 NA
8 NA
[1] "ID" "GP1" "GP2"
[4] "GP3" "GP4" "GP5"
[7] "GP6" "GP7" "GS1"
[10] "GS2" "GS3" "GS4"
[13] "GS5" "GS6" "GS7"
[16] "GE1" "GE2" "GE3"
[19] "GE4" "GE5" "GE6"
[22] "GF1" "GF2" "GF3"
[25] "GF4" "GF5" "GF6"
[28] "GF7" "C2" "C3"
[31] "C4" "C6" "C7"
[34] "BL1" "ITU7" "ITU6"
[37] "C9" "ITU3" "ITU4"
[40] "ITU5" "ITU1" "VC1"
[43] "ITU2" "PWB_N" "SWB_N"
[46] "EWB_N" "FWB_N" "FACTG_N"
[49] "PWB" "SWB" "EWB"
[52] "FWB" "FACTG" "BL_CYS_N"
[55] "FACT_BL_CYS_TOTAL_N" "BL_CYS" "FACT_BL_CYS_TOTAL"
[58] "FACT_BL_CYS_TOI"
PWB SWB EWB FWB
Min. :12.0 Min. : 7.00 Min. : 7.20 Min. : 5.833
1st Qu.:13.5 1st Qu.:10.88 1st Qu.:11.55 1st Qu.: 8.500
Median :15.0 Median :13.00 Median :13.10 Median :10.250
Mean :14.5 Mean :12.79 Mean :12.60 Mean :11.128
3rd Qu.:16.0 3rd Qu.:15.40 3rd Qu.:14.15 3rd Qu.:12.075
Max. :16.0 Max. :17.00 Max. :17.00 Max. :19.833
NA's :4 NA's :1 NA's :4 NA's :2
FACTG BL_CYS FACT_BL_CYS_TOTAL FACT_BL_CYS_TOI
Min. :44.00 Min. :18.33 Min. :65.92 Min. :45.92
1st Qu.:44.15 1st Qu.:25.34 1st Qu.:72.93 1st Qu.:46.42
Median :48.02 Median :31.07 Median :77.93 Median :50.83
Mean :50.52 Mean :28.47 Mean :79.03 Mean :53.93
3rd Qu.:54.38 3rd Qu.:32.09 3rd Qu.:84.02 3rd Qu.:58.34
Max. :62.03 Max. :35.00 Max. :94.34 Max. :68.14
NA's :4 NA's :1 NA's :4 NA's :4
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.