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The PROscorer package is an extensible repository of functions to score specific patient-reported outcome (PRO), quality of life (QoL), and other psychometric measures and questionnaire-based instruments commonly used in research.

(Note: For simplicity, from here forward I will collectively and somewhat imprecisely refer to these types of instruments as “PRO measures”, “PRO-like instruments”, or just “PROs”.)

PROscorer is intended to standardize scoring procedures for PRO measures across studies, to promote best practices for scoring PRO-like instruments, and to improve the reproducibility of research with PRO-like instruments by providing accurate, up-to-date, and well-documented PRO scoring functions that can easily be integrated into scientifically reproducible workflows.

Each function in the PROscorer package scores a different PRO measure. Functions are named using the initials of the PRO measure. For example, the fsfi function scores the Female Sexual Function Index (FSFI).

PROscorer also comes with a vignette containing detailed descriptions of each of the instruments scored by PROscorer (see main PROscorer page on CRAN). The purpose of including these instrument descriptions, complete with references, is to help improve the descriptions of PRO measures in protocols, grants, and published results. In most cases, the descriptions can be used in research documents with little or no editing.

To minimize the possibility of scoring errors and other bugs, each PROscorer function is composed of simpler, well-tested “helper” functions from the PROscorerTools package. This reliance on a small set of simple functions that have been thoroughly tested ensures that the underlying code base of PROscorer functions is bug-free, and that the scoring functions produce reliable, consistent, and accurate results.

PROscorer, together with the PROscorerTools package, is a system to facilitate the incorporation of PRO measures into research studies and clinical settings in a scientifically rigorous and reproducible manner. The overarching goals of the PROscorer and PROscorerTools packages are to draw attention to PRO scoring and reporting best-practices and to help eliminate inaccurate and inconsistent scoring.

The current version is still somewhat developmental, since the formal unit testing system is still immature for some functions, and not yet in place for others. Please use with caution at this time, and feel free to contact me with questions or suggestions. More scoring functions are currently in development and will be added in future updates, including more functions for the EORTC family of PRO measures.

Installation and Usage

Install the stable version of PROscorer from CRAN:


Load PROscorer into your R workspace with the following:


As an example, we will use the makeFakeData function from the PROscorerTools package to make fake item responses to the EORTC QLQ-C30 quality of life questionnaire. The created data set (named “dat”) has an “id” variable, plus responses to 30 items (named “q1”, “q2”, etc.) from 20 imaginary respondents. There are also missing responses (“NA”) scattered throughout.

dat <- PROscorerTools::makeFakeData(n = 20, nitems = 30, values = 1:4, id = TRUE)

Below we will use the qlq_c30 function to score the fake responses in “dat”. We will save the scores from the EORTC QLQ-C30 questionnaire in a data frame named “c30scores”.

c30scores <- qlq_c30(dat, 'q')

The first argument to qlq_c30 took our data frame, “dat”. With the second argument, we needed to tell the qlq_c30 function how to find our items in “dat”. Since our items are all named with the prefix “q” plus the item number, we gave this quoted prefix to the second argument. These arguments actually have names, but in most cases you don’t have to explicitly use the names. Below gives the same results, but explicitly uses the argument names.

c30scores <- qlq_c30(df = dat, iprefix = 'q')

Specifically, the first argument is named df (for data frame) and the second is named iprefix (for item prefix).

If you want to merge your scores back into your main data frame with the item responses, there are several different ways to do so. For example, assuming you have not changed the order of dat or dat_scored, you can do the following:

{r eval=FALSE} dat_scored <- data.frame(dat, c30scores) dat_scored

For more information on the qlq_c30 function, you can access its “help” page by typing ?qlq_c30 into R.

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