scoreFACT_M: Score the FACT-M
In FACTscorer: Scores the FACT and FACIT Family of Patient-Reported Outcome Measures
Description
Usage
Arguments
Details
Value
Note
References
Examples
View source: R/ca-scoreFACT_M.R
Description
Generates all of the scores of the Functional Assessment of Cancer Therapy -
Melanoma (FACT-M, v4) from item responses.
Usage
1
scoreFACT_M(df, updateItems = FALSE, keepNvalid = FALSE)
Arguments
df
A data frame with the FACT-M 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 most 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-M (Version 4) items as
variables, appropriately named, this function generates all of the FACT-M
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)
- MS
Melanoma subscale
- MSS
Melanoma Surgery Scale
- FACT_M_TOTAL
FACT-M Total Score (i.e., PWB+SWB+EWB+FWB+MS+MSS)
- FACT_M_TOI
FACT-M Trial Outcome Index (e.g., PWB+FWB+MS+MSS)
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_M 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_M.
References
FACT-M Scoring Guidelines, available at http://www.facit.org
Examples
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## 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('M1', 'M2', 'M3', 'B1', 'ITU4', 'An10', 'Hep3', 'C1', 'C6',
'M5', 'M6', 'ITU3', 'MS8', 'M8', 'M9', 'HI7')
AC_names2 <- c('M10', 'M11', 'M12', 'M13', 'M14', 'M15', 'M16', 'M17')
AC_names <- c(AC_names1, AC_names2)
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_M(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_M(exampleDat, updateItems = TRUE, keepNvalid = TRUE)
names(scoredDat)
## Descriptives of scored scales
summary(scoredDat[, c('PWB', 'SWB', 'EWB', 'FWB', 'FACTG',
'MS', 'MSS', 'FACT_M_TOTAL', 'FACT_M_TOI')])
Example output
[1] "ID" "GP1" "GP2" "GP3" "GP4"
[6] "GP5" "GP6" "GP7" "GS1" "GS2"
[11] "GS3" "GS4" "GS5" "GS6" "GS7"
[16] "GE1" "GE2" "GE3" "GE4" "GE5"
[21] "GE6" "GF1" "GF2" "GF3" "GF4"
[26] "GF5" "GF6" "GF7" "M1" "M2"
[31] "M3" "B1" "ITU4" "An10" "Hep3"
[36] "C1" "C6" "M5" "M6" "ITU3"
[41] "MS8" "M8" "M9" "HI7" "M10"
[46] "M11" "M12" "M13" "M14" "M15"
[51] "M16" "M17" "PWB" "SWB" "EWB"
[56] "FWB" "FACTG" "MS" "MSS" "FACT_M_TOTAL"
[61] "FACT_M_TOI"
ID GP1 GP2 GP3 GP4 GP5 GP6 GP7 GS1 GS2 GS3 GS4 GS5 GS6 GS7 GE1 GE2 GE3 GE4
1 ID1 9 3 1 2 9 3 0 1 2 1 1 1 0 2 0 2 0 2
2 ID2 4 0 0 3 3 3 2 0 2 2 3 3 4 4 0 0 3 3
3 ID3 4 4 9 1 9 2 0 4 0 9 2 9 2 4 3 3 1 1
4 ID4 3 2 3 2 0 0 2 3 2 3 0 0 0 0 0 1 3 3
5 ID5 9 3 9 9 9 0 3 9 2 1 9 0 2 9 4 9 3 0
6 ID6 9 1 3 9 1 3 1 9 9 3 9 0 9 9 9 9 1 2
7 ID7 1 9 9 9 9 4 9 0 9 4 2 9 9 3 9 9 2 3
8 ID8 9 1 9 9 9 0 9 9 9 1 9 4 9 9 2 9 9 2
GE5 GE6 GF1 GF2 GF3 GF4 GF5 GF6 GF7 M1 M2 M3 B1 ITU4 An10 Hep3 C1 C6 M5 M6
1 4 3 0 3 2 0 1 2 2 9 2 1 2 3 0 4 4 4 3 0
2 3 2 3 3 2 3 0 0 2 9 0 2 2 2 9 0 0 3 3 2
3 1 4 1 2 0 2 1 2 2 2 3 2 4 2 1 1 4 3 4 2
4 3 0 4 4 4 1 2 4 0 1 9 3 9 2 0 0 2 0 2 3
5 9 1 2 9 0 9 9 4 1 2 9 2 9 9 9 9 9 9 1 9
6 9 0 9 1 4 3 3 9 9 0 9 4 0 3 9 2 9 3 3 0
7 0 9 0 2 2 9 4 2 2 0 4 0 0 9 9 9 9 3 3 3
8 1 9 2 9 9 9 9 0 0 9 9 3 2 9 1 9 9 9 3 4
ITU3 MS8 M8 M9 HI7 M10 M11 M12 M13 M14 M15 M16 M17 PWB SWB EWB FWB FACTG
1 4 9 2 4 2 2 3 1 2 1 1 0 3 15.4 8.00 13 10.00 46.4
2 2 2 1 9 4 9 3 0 2 1 1 2 3 13.0 18.00 9 13.00 53.0
3 0 1 0 2 1 1 0 0 1 1 1 1 2 12.6 16.80 13 10.00 52.4
4 3 0 2 9 1 0 2 2 0 3 1 4 2 16.0 8.00 12 19.00 55.0
5 2 9 9 3 4 4 9 9 9 1 3 9 4 NA 8.75 12 12.25 NA
6 4 0 4 9 1 9 2 9 9 9 2 3 0 15.4 NA NA 19.25 NA
7 4 4 0 3 9 9 9 9 3 9 4 3 1 NA 15.75 NA 14.00 NA
8 4 9 9 9 9 0 9 2 9 4 1 9 9 NA NA NA NA NA
MS MSS FACT_M_TOTAL FACT_M_TOI
1 28.571 21.000 74.971 53.971
2 38.154 20.571 91.154 64.154
3 34.000 25.000 86.400 56.600
4 35.692 18.000 90.692 70.692
5 NA NA NA NA
6 34.667 NA NA 69.317
7 32.000 NA NA NA
8 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] "M1" "M2" "M3" "B1"
[33] "ITU4" "AN10" "HEP3" "C1"
[37] "C6" "M5" "M6" "ITU3"
[41] "MS8" "M8" "M9" "HI7"
[45] "M10" "M11" "M12" "M13"
[49] "M14" "M15" "M16" "M17"
[53] "PWB_N" "SWB_N" "EWB_N" "FWB_N"
[57] "FACTG_N" "PWB" "SWB" "EWB"
[61] "FWB" "FACTG" "MS_N" "MSS_N"
[65] "FACT_M_TOTAL_N" "MS" "MSS" "FACT_M_TOTAL"
[69] "FACT_M_TOI"
PWB SWB EWB FWB FACTG
Min. :12.60 Min. : 8.000 Min. : 9.0 Min. :10.00 Min. :46.4
1st Qu.:13.00 1st Qu.: 8.188 1st Qu.:12.0 1st Qu.:11.12 1st Qu.:50.9
Median :15.40 Median :12.250 Median :12.0 Median :13.00 Median :52.7
Mean :14.48 Mean :12.550 Mean :11.8 Mean :13.93 Mean :51.7
3rd Qu.:15.40 3rd Qu.:16.538 3rd Qu.:13.0 3rd Qu.:16.50 3rd Qu.:53.5
Max. :16.00 Max. :18.000 Max. :13.0 Max. :19.25 Max. :55.0
NA's :3 NA's :2 NA's :3 NA's :1 NA's :4
MS MSS FACT_M_TOTAL FACT_M_TOI
Min. :28.57 Min. :18.00 Min. :74.97 Min. :53.97
1st Qu.:32.50 1st Qu.:19.93 1st Qu.:83.54 1st Qu.:56.60
Median :34.33 Median :20.79 Median :88.55 Median :64.15
Mean :33.85 Mean :21.14 Mean :85.80 Mean :62.95
3rd Qu.:35.44 3rd Qu.:22.00 3rd Qu.:90.81 3rd Qu.:69.32
Max. :38.15 Max. :25.00 Max. :91.15 Max. :70.69
NA's :2 NA's :4 NA's :4 NA's :3
FACTscorer documentation built on May 29, 2017, 3:45 p.m.
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Description Usage Arguments Details Value Note References Examples
View source: R/ca-scoreFACT_M.R
Description
Generates all of the scores of the Functional Assessment of Cancer Therapy - Melanoma (FACT-M, v4) from item responses.
Usage
1 | scoreFACT_M(df, updateItems = FALSE, keepNvalid = FALSE)
|
Arguments
df |
A data frame with the FACT-M items, appropriately-named. |
updateItems |
Logical, if |
keepNvalid |
Logical, if |
Details
Given a data frame that includes all of the FACT-M (Version 4) items as variables, appropriately named, this function generates all of the FACT-M 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)
- MS
Melanoma subscale
- MSS
Melanoma Surgery Scale
- FACT_M_TOTAL
FACT-M Total Score (i.e., PWB+SWB+EWB+FWB+MS+MSS)
- FACT_M_TOI
FACT-M Trial Outcome Index (e.g., PWB+FWB+MS+MSS)
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_M 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_M.
References
FACT-M 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 37 38 39 | ## 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('M1', 'M2', 'M3', 'B1', 'ITU4', 'An10', 'Hep3', 'C1', 'C6',
'M5', 'M6', 'ITU3', 'MS8', 'M8', 'M9', 'HI7')
AC_names2 <- c('M10', 'M11', 'M12', 'M13', 'M14', 'M15', 'M16', 'M17')
AC_names <- c(AC_names1, AC_names2)
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_M(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_M(exampleDat, updateItems = TRUE, keepNvalid = TRUE)
names(scoredDat)
## Descriptives of scored scales
summary(scoredDat[, c('PWB', 'SWB', 'EWB', 'FWB', 'FACTG',
'MS', 'MSS', 'FACT_M_TOTAL', 'FACT_M_TOI')])
|
Example output
[1] "ID" "GP1" "GP2" "GP3" "GP4"
[6] "GP5" "GP6" "GP7" "GS1" "GS2"
[11] "GS3" "GS4" "GS5" "GS6" "GS7"
[16] "GE1" "GE2" "GE3" "GE4" "GE5"
[21] "GE6" "GF1" "GF2" "GF3" "GF4"
[26] "GF5" "GF6" "GF7" "M1" "M2"
[31] "M3" "B1" "ITU4" "An10" "Hep3"
[36] "C1" "C6" "M5" "M6" "ITU3"
[41] "MS8" "M8" "M9" "HI7" "M10"
[46] "M11" "M12" "M13" "M14" "M15"
[51] "M16" "M17" "PWB" "SWB" "EWB"
[56] "FWB" "FACTG" "MS" "MSS" "FACT_M_TOTAL"
[61] "FACT_M_TOI"
ID GP1 GP2 GP3 GP4 GP5 GP6 GP7 GS1 GS2 GS3 GS4 GS5 GS6 GS7 GE1 GE2 GE3 GE4
1 ID1 9 3 1 2 9 3 0 1 2 1 1 1 0 2 0 2 0 2
2 ID2 4 0 0 3 3 3 2 0 2 2 3 3 4 4 0 0 3 3
3 ID3 4 4 9 1 9 2 0 4 0 9 2 9 2 4 3 3 1 1
4 ID4 3 2 3 2 0 0 2 3 2 3 0 0 0 0 0 1 3 3
5 ID5 9 3 9 9 9 0 3 9 2 1 9 0 2 9 4 9 3 0
6 ID6 9 1 3 9 1 3 1 9 9 3 9 0 9 9 9 9 1 2
7 ID7 1 9 9 9 9 4 9 0 9 4 2 9 9 3 9 9 2 3
8 ID8 9 1 9 9 9 0 9 9 9 1 9 4 9 9 2 9 9 2
GE5 GE6 GF1 GF2 GF3 GF4 GF5 GF6 GF7 M1 M2 M3 B1 ITU4 An10 Hep3 C1 C6 M5 M6
1 4 3 0 3 2 0 1 2 2 9 2 1 2 3 0 4 4 4 3 0
2 3 2 3 3 2 3 0 0 2 9 0 2 2 2 9 0 0 3 3 2
3 1 4 1 2 0 2 1 2 2 2 3 2 4 2 1 1 4 3 4 2
4 3 0 4 4 4 1 2 4 0 1 9 3 9 2 0 0 2 0 2 3
5 9 1 2 9 0 9 9 4 1 2 9 2 9 9 9 9 9 9 1 9
6 9 0 9 1 4 3 3 9 9 0 9 4 0 3 9 2 9 3 3 0
7 0 9 0 2 2 9 4 2 2 0 4 0 0 9 9 9 9 3 3 3
8 1 9 2 9 9 9 9 0 0 9 9 3 2 9 1 9 9 9 3 4
ITU3 MS8 M8 M9 HI7 M10 M11 M12 M13 M14 M15 M16 M17 PWB SWB EWB FWB FACTG
1 4 9 2 4 2 2 3 1 2 1 1 0 3 15.4 8.00 13 10.00 46.4
2 2 2 1 9 4 9 3 0 2 1 1 2 3 13.0 18.00 9 13.00 53.0
3 0 1 0 2 1 1 0 0 1 1 1 1 2 12.6 16.80 13 10.00 52.4
4 3 0 2 9 1 0 2 2 0 3 1 4 2 16.0 8.00 12 19.00 55.0
5 2 9 9 3 4 4 9 9 9 1 3 9 4 NA 8.75 12 12.25 NA
6 4 0 4 9 1 9 2 9 9 9 2 3 0 15.4 NA NA 19.25 NA
7 4 4 0 3 9 9 9 9 3 9 4 3 1 NA 15.75 NA 14.00 NA
8 4 9 9 9 9 0 9 2 9 4 1 9 9 NA NA NA NA NA
MS MSS FACT_M_TOTAL FACT_M_TOI
1 28.571 21.000 74.971 53.971
2 38.154 20.571 91.154 64.154
3 34.000 25.000 86.400 56.600
4 35.692 18.000 90.692 70.692
5 NA NA NA NA
6 34.667 NA NA 69.317
7 32.000 NA NA NA
8 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] "M1" "M2" "M3" "B1"
[33] "ITU4" "AN10" "HEP3" "C1"
[37] "C6" "M5" "M6" "ITU3"
[41] "MS8" "M8" "M9" "HI7"
[45] "M10" "M11" "M12" "M13"
[49] "M14" "M15" "M16" "M17"
[53] "PWB_N" "SWB_N" "EWB_N" "FWB_N"
[57] "FACTG_N" "PWB" "SWB" "EWB"
[61] "FWB" "FACTG" "MS_N" "MSS_N"
[65] "FACT_M_TOTAL_N" "MS" "MSS" "FACT_M_TOTAL"
[69] "FACT_M_TOI"
PWB SWB EWB FWB FACTG
Min. :12.60 Min. : 8.000 Min. : 9.0 Min. :10.00 Min. :46.4
1st Qu.:13.00 1st Qu.: 8.188 1st Qu.:12.0 1st Qu.:11.12 1st Qu.:50.9
Median :15.40 Median :12.250 Median :12.0 Median :13.00 Median :52.7
Mean :14.48 Mean :12.550 Mean :11.8 Mean :13.93 Mean :51.7
3rd Qu.:15.40 3rd Qu.:16.538 3rd Qu.:13.0 3rd Qu.:16.50 3rd Qu.:53.5
Max. :16.00 Max. :18.000 Max. :13.0 Max. :19.25 Max. :55.0
NA's :3 NA's :2 NA's :3 NA's :1 NA's :4
MS MSS FACT_M_TOTAL FACT_M_TOI
Min. :28.57 Min. :18.00 Min. :74.97 Min. :53.97
1st Qu.:32.50 1st Qu.:19.93 1st Qu.:83.54 1st Qu.:56.60
Median :34.33 Median :20.79 Median :88.55 Median :64.15
Mean :33.85 Mean :21.14 Mean :85.80 Mean :62.95
3rd Qu.:35.44 3rd Qu.:22.00 3rd Qu.:90.81 3rd Qu.:69.32
Max. :38.15 Max. :25.00 Max. :91.15 Max. :70.69
NA's :2 NA's :4 NA's :4 NA's :3
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