fsfi: Score the Female Sexual Function Index (FSFI)

Description Usage Arguments Details Value How Missing Data is Handled Note References Examples

View source: R/fsfi.R

Description

Scores the Female Sexual Function Index (FSFI)

Usage

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fsfi(df, iprefix = "fsfi", keepNvalid = FALSE)

Arguments

df

A data frame containing responses to the 19 FSFI items, and possibly other variables.

iprefix

Item number prefix. Quote the letter(s) preceding the FSFI item numbers as they are named in your data frame. If this argument is omitted, the function will assume that your items are named "fsfi1", "fsfi2", etc.

keepNvalid

Logical, whether to return variables containing the number of valid, non-missing items on each scale for each respondent should be returned in the data frame with the scale scores. The default is FALSE. Set to TRUE to return these variables, which will be named "scalename_N" (e.g., fsfi_pain_N). Most users should omit this argument entirely. This argument might be removed from future versions of the package, so please let me know if you think this argument useful and would rather it remain a part of the function.

Details

This function returns the 6 subscale scores and the FSFI Total score (Rosen et al., 2000), as well as an indicator variable flagging respondents with FSFI Total scores suggestive of clinically significant levels of sexual dysfunction (i.e., fsfi_tot <= 26.55; Wiegel et al., 2005).

The FSFI is intended to measure the sexual function of recently sexually active women (Rosen et al., 2000), and strong evidence suggests it may not be a valid measure of sexual function in women with little or no recent sexual activity (e.g., see Baser et al., 2012).

As such the fsfi function also returns two variables (fsfi_nzero15 and fsfi_sexactive01) that can be used to evaluate whether respondents have been sufficiently sexually active for the FSFI to be a valid assessment of their sexual function. These variables are based on the fact that 15 of the 19 FSFI items have a response option of "no sexual activity" or "did not attempt intercourse", which corresponds to an item score of 0. Specifically, the fsfi_nzero15 variable contains the number of items with responses of 0 or NA (out of those 15 items that have a response option indicating "no sexual activity"). Missing responses (i.e., NA) are included in this count because respondents with no relevant sexual activity often skip these items. The fsfi_sexactive01 variable is a rough indicator that a respondent was sufficiently sexually active for the FSFI to be a valid assessment of their sexual function. It is a dummy variable that is 1 when fsfi_nzero15 <= 7 (i.e., when the respondent said "no sexual activity" to 7 or fewer of the 15 items with that option), and 0 otherwise. See Baser et al. (2012) for more details on how this cutoff was chosen.

Value

A data frame with the following variables is returned:

Optionally, the data frame can additionally have variables containing the number of valid item responses on each scale for each respondent (if keepNvalid = TRUE, but this option might be removed in future package updates).

How Missing Data is Handled

The FSFI authors do not indicate how to handle missing item data when calculating the FSFI scores. This is unfortunate because women frequently skip items they feel are not relevant to them (e.g., the items asking about satisfaction with "your partner" are often skipped by non-partnered women), leading to an unexpectedly large number of missing subscale and FSFI total scores. To minimize excessive missing values for the FSFI subscale and Total scores, the fsfi function handles missing items similarly to the scoring methods for many other PROs. Specifically, the fsfi function will calculate the 6 subscale scores as long as at least half of the items on the given subscale have valid, non-missing item responses. More concretely, each subscale must have at least 2 non-missing responses, except for Desire, which has only 2 items and requires only 1 non-missing response. The fsfi function will calculate the FSFI Total Score for a respondent as long as it was able to calculate at least 5 out of the 6 subscale scores. Scores calculated in the presence of missing items are pro-rated so that their theoretical minimum and maximum values are identical those from scores calculated from complete data.

These methods of handling missing item responses were chosen to balance the reality that respondents often skip some items with the need to maintain the validity of the scores. However, I know of no directly applicable empirical study that supports these choices, and I encourage more research into how missing responses affect the psychometrics of this and other instruments.

Note

The six FSFI subscale scores are scaled to have a maximum score of 6.0. The subscale scores are summed to calculate the FSFI Total score, which has a maximum score of 36. Because 4 items have no response option scored 0 (2 items from Desire subscale and 2 from Satisfaction subscale), the minimum possible score for the Desire subscale, the Satisfaction subscale, and the FSFI Total score is greater than zero.

References

Rosen, R, Brown, C, Heiman, J, Leiblum, S, Meston, C, Shabsigh, R, et al. (2000). The Female Sexual Function Index (FSFI): a multidimensional self-report instrument for the assessment of female sexual function. Journal of Sex & Marital Therapy, 26(2), 191-208.

Wiegel, M, Meston, C, & Rosen, R. (2005). The Female Sexual Function Index (FSFI): Cross-Validation and Development of Clinical Cutoff Scores. Journal of Sex & Marital Therapy, 31(1), 1-20.

Baser, RE, Li, Y, & Carter, J. (2012). Psychometric validation of the female sexual function index (FSFI) in cancer survivors. Cancer, 118(18), 4606-4618.

Examples

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# Creating data frame of fake FSFI responses
dat <- PROscorerTools::makeFakeData(n = 10, nitems = 19, values = 0:5,
                                    prefix = 'f')
dat1 <- PROscorerTools::makeFakeData(n = 10, nitems = 4, values = 1:5)
names(dat1) <- c('f1', 'f2', 'f15', 'f16')
dat[c(1, 2, 15, 16)] <- dat1
# Scoring the fake FSFI responses
fsfi(dat, 'f')

Example output

   fsfi_des fsfi_arous fsfi_lub fsfi_org fsfi_sat fsfi_pain fsfi_tot fsfi_dys01
1       1.2        0.0      0.0      0.0      0.8       0.0     2.00          1
2       6.0        6.0      6.0      6.0      6.0       6.0    36.00          0
3       2.4        3.0      4.0      3.2      3.6       4.0    20.20          1
4       5.4        0.8      2.0      4.4       NA       5.4    21.60          1
5       3.6        2.4      4.4      2.8      4.4       2.4    20.00          1
6       3.6        3.2      1.8      4.2      4.0       0.8    17.60          1
7       1.2        0.0       NA      4.8      4.8       3.2    16.80          1
8       1.2        2.0      3.2      1.6      3.6       3.2    14.80          1
9       2.4        3.6      1.2      2.4      3.0       3.0    15.60          1
10       NA        1.2      3.2      3.2      2.8       3.2    16.32          1
   fsfi_nzero15 fsfi_sexactive01
1            15                0
2             0                1
3             3                1
4             7                1
5             4                1
6             9                0
7             7                1
8             2                1
9             4                1
10            4                1

PROscorer documentation built on May 29, 2017, 7:54 p.m.