scoreFACT_COG: Score the FACT-COG

Description Usage Arguments Details Value Note References Examples

View source: R/sx-scoreFACT_COG.R

Description

Generates all of the scores of the Functional Assessment of Cancer Therapy - Cognitive function issues - Patient Satisfaction (FACT-COG, v4) from item responses.

Usage

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scoreFACT_COG(df, updateItems = FALSE, keepNvalid = FALSE)

Arguments

df

A data frame with the FACT-COG 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-COG (Version 4) items as variables, appropriately named, this function generates all of the FACT-COG scale scores. It is crucial that the item variables in the supplied data frame are named according to FACT conventions. For example, the first Perceived Cognitive Impairments item should be named CogA1, the second CogA3, 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:

CogPCI

Perceived Cognitive Impairments subscale

CogQOL

Impact of perceived cognitive impairments on quality of life subscale

CogOth

Comments from Others subscale

CogPCA

Perceived Cognitive Abilities subscale

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_COG 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_COG.

References

FACT-COG Scoring Guidelines, available at http://www.facit.org

Examples

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## Setting up item names for fake data
AC_names1 <- c('CogA1', 'CogA3', 'CogC7', 'CogM9', 'CogM10', 'CogM12', 'CogV13',
               'CogV15', 'CogV16', 'CogV17b', 'CogF19', 'CogF23', 'CogF24', 'CogF25',
               'CogC31', 'CogC32', 'CogC33a', 'CogC33c')
AC_names2 <- c('CogQ35', 'CogQ37', 'CogQ38', 'CogQ41')
AC_names3 <- c('CogO1', 'CogO2', 'CogO3', 'CogO4')
AC_names4 <- c('CogPC1', 'CogPV1', 'CogPM1','CogPM2', 'CogPF1', 'CogPCh1', 'CogPCh2')
AC_names <- c(AC_names1, AC_names2, AC_names3, AC_names4)
itemNames <- 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_COG(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_COG(exampleDat, updateItems = TRUE, keepNvalid = TRUE)
names(scoredDat)
## Descriptives of scored scales
summary(scoredDat[, c('CogPCI', 'CogQOL', 'CogOth', 'CogPCA')])

Example output

 [1] "ID"      "CogA1"   "CogA3"   "CogC7"   "CogM9"   "CogM10"  "CogM12" 
 [8] "CogV13"  "CogV15"  "CogV16"  "CogV17b" "CogF19"  "CogF23"  "CogF24" 
[15] "CogF25"  "CogC31"  "CogC32"  "CogC33a" "CogC33c" "CogQ35"  "CogQ37" 
[22] "CogQ38"  "CogQ41"  "CogO1"   "CogO2"   "CogO3"   "CogO4"   "CogPC1" 
[29] "CogPV1"  "CogPM1"  "CogPM2"  "CogPF1"  "CogPCh1" "CogPCh2" "CogPCI" 
[36] "CogQOL"  "CogOth"  "CogPCA" 
   ID CogA1 CogA3 CogC7 CogM9 CogM10 CogM12 CogV13 CogV15 CogV16 CogV17b CogF19
1 ID1     3     3     1     2      2      9      9      1      2       1      1
2 ID2     4     4     4     3      0      4      3      2      4       2      2
3 ID3     0     3     3     3      9      3      3      2      3       0      0
4 ID4     1     9     9     4      4      0      1      1      2       0      4
5 ID5     4     9     1     9      9      3      9      2      0       9      9
6 ID6     9     9     9     9      3      9      3      9      9       4      9
7 ID7     9     9     3     9      9      9      9      9      9       1      1
8 ID8     2     2     9     1      9      3      2      2      9       1      9
  CogF23 CogF24 CogF25 CogC31 CogC32 CogC33a CogC33c CogQ35 CogQ37 CogQ38
1      1      0      9      0      2       0       9      4      3      0
2      3      9      2      9      1       0       3      9      0      0
3      2      4      0      9      2       2       1      0      0      3
4      0      3      2      3      2       4       3      3      1      1
5      9      1      9      0      9       1       9      9      9      2
6      1      9      9      9      1       4       9      1      2      0
7      9      9      9      9      1       0       9      9      9      9
8      9      9      1      9      9       4       0      1      4      9
  CogQ41 CogO1 CogO2 CogO3 CogO4 CogPC1 CogPV1 CogPM1 CogPM2 CogPF1 CogPCh1
1      3     2     0     1     2      2      4      2      9      2       3
2      3     3     3     2     0      2      2      3      3      4       4
3      3     2     2     2     1      0      4      3      3      0       2
4      1     4     1     2     0      2      1      2      9      2       3
5      9     9     9     2     0      0      9      3      9      3       9
6      0     2     9     9     3      9      0      9      0      1       9
7      9     3     0     2     9      9      4      0      1      4       9
8      3     9     4     4     9      3      9      9      3      1       1
  CogPCh2 CogPCI CogQOL CogOth CogPCA
1       9 47.571  6.000 11.000  18.20
2       0 25.875 12.000  8.000  18.00
3       1 37.125 10.000  9.000  13.00
4       2 33.750 10.000  9.000  14.00
5       0     NA     NA     NA  10.50
6       9     NA 13.000     NA     NA
7       9     NA     NA  9.333  15.75
8       9 39.600  5.333     NA  14.00
 [1] "ID"       "COGA1"    "COGA3"    "COGC7"    "COGM9"    "COGM10"  
 [7] "COGM12"   "COGV13"   "COGV15"   "COGV16"   "COGV17B"  "COGF19"  
[13] "COGF23"   "COGF24"   "COGF25"   "COGC31"   "COGC32"   "COGC33A" 
[19] "COGC33C"  "COGQ35"   "COGQ37"   "COGQ38"   "COGQ41"   "COGO1"   
[25] "COGO2"    "COGO3"    "COGO4"    "COGPC1"   "COGPV1"   "COGPM1"  
[31] "COGPM2"   "COGPF1"   "COGPCH1"  "COGPCH2"  "CogPCI_N" "CogQOL_N"
[37] "CogOth_N" "CogPCA_N" "CogPCI"   "CogQOL"   "CogOth"   "CogPCA"  
     CogPCI          CogQOL           CogOth           CogPCA     
 Min.   :25.88   Min.   : 5.333   Min.   : 8.000   Min.   :10.50  
 1st Qu.:33.75   1st Qu.: 7.000   1st Qu.: 9.000   1st Qu.:13.50  
 Median :37.12   Median :10.000   Median : 9.000   Median :14.00  
 Mean   :36.78   Mean   : 9.389   Mean   : 9.267   Mean   :14.78  
 3rd Qu.:39.60   3rd Qu.:11.500   3rd Qu.: 9.333   3rd Qu.:16.88  
 Max.   :47.57   Max.   :13.000   Max.   :11.000   Max.   :18.20  
 NA's   :3       NA's   :2        NA's   :3        NA's   :1      

FACTscorer documentation built on May 29, 2017, 3:45 p.m.