qlq_blm30: Score the EORTC QLQ-BLM30 Quality of Life Questionnaire

In raybaser/PROscorer: Functions to Score Commonly-Used Patient-Reported Outcome (PRO) Measures and Other Psychometric Instruments

Score the EORTC QLQ-BLM30 Quality of Life Questionnaire

Description

Scores the European Organization for Research and Treatment of Cancer (EORTC) QLQ-BLM30 Muscle-Invasive Bladder Cancer Module.

Usage

qlq_blm30(df, items = NULL, keepNvalid = FALSE)

Arguments

df	A data frame containing responses to the 30 QLQ-BLM30 items, and possibly other variables.
items	A character vector with the QLQ-BLM30 item names, or a numeric vector indicating the column numbers of the QLQ-BLM30 items in df. If items is omitted, then qlq_blm30 will assume that df contains ONLY the QLQ-BLM30 items and no other variables. See Details for more information.
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., QL_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 a total of 7 different scores from the EORTC QLQ-BLM30. Scores are calculated according to the official scoring algorithms from the EORTC.

In addition to the name of your data frame containing the QLQ-BLM30 item responses (df), you need to tell the function how to find the variables that correspond to the QLQ-BLM30 items in df. You can do this in 1 of 2 ways:

1. The first way is to manually provide the item names or locations using the items argument. For example, if your first 10 variables in df contain demographics, followed by the 30 QLQ-BLM30 items in order starting with the 11th variable, then you could use items = 11:40.

2. The second way only applies if your data frame (df) contains ONLY the 30 variables corresponding to the 30 QLQ-BLM30 items, in order, with no other non-QLQ-BLM30 variables. In this case, you can just use the df argument and omit items.

Value

A data frame with all 7 of the QLQ-BLM30 scores is returned. Of the 7 scores, there are 5 multi-item Symptom Scales, 1 single-item Symptom Scale (Catheter Use Problems), and 1 multi-item Functional Scale (Sexual Functioning) (see below). All scores are scaled to range from 0-100, even scores based on single items. Be aware that these single-item scales still have only 4 possible values, even though they are transformed to range from 0-100. The scale names and numbers of items are listed below.

Symptom Scales (higher is more symptoms, worse functioning)

• BLM_US - Urinary Symptoms (from 7 items)

• BLM_UP - Urostomy Problems (from 6 items)

• BLM_FU - Future Perspective (from 3 items)

• BLM_BAF - Abdominal Bloating and Flatulence (from 2 items)

• BLM_BI - Body Image (from 3 items)

• BLM_CU - Catheter Use Problems (from 1 item)

Functional Scales (higher is better functioning)

• BLM_SX - Sexual Functioning (from 2, 4, 6, or 7 items, depending on the respondent)

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

For all scale scores except for the Sexual Functioning scale, the qlq_blm30 function will calculate the scale scores as long as at least half of the items on the given scale have valid, non-missing item responses. Scores calculated in the presence of missing items are pro-rated so that their theoretical minimum and maximum values are identical to those from scores calculated from complete data.

The Sexual Functioning scale is handled differently because it has several conditional questions that may not be applicable to a given patient (i.e., some questions apply only to men or only to women, and some questions apply only to sexually active respondents). As a result, the Sexual Functioning scale may have 2, 4, 6, or 7 questions applicable for a given respondent. Because the minimum number of applicable items is 2, the qlq_blm30 function will calculate the Sexual Functioning scale score as long as at least 1 of the items has a valid, non-missing response. Note that this can result in the qlq_blm30 function scoring the Sexual Functioning scale for some respondents when they should technically be assigned a missing value for the scale.

References

The QLQ-BLM30 validation study does not appear to be publicly available.

Rammant E, Fox L, Beyer K, et al. The current use of the EORTC QLQ-NMIBC24 and QLQ-BLM30 questionnaires for the assessment of health-related quality of life in bladder cancer patients: a systematic review. Qual Life Res. Published online January 17, 2023. doi:10.1007/s11136-022-03335-4

Examples

## Not run: 
dat <- PROscorerTools::makeFakeData(n = 10, nitems = 30, prefix = "blm", values = 1:4)
qlq_blm30(dat, items = 1:30)

## End(Not run)

raybaser/PROscorer documentation built on Oct. 17, 2023, 8:48 p.m.