knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "README-" )
PROscorerTools provides tools to score patient-reported outcome (PRO) measures and other quality of life (QoL) and psychometric instruments. PROscorerTools also provides the building blocks of the functions in the PROscorer package.
PROscorerTools contains several "helper" functions, each of which performs a
specific task that is common when scoring PRO-like instruments (e.g., reverse
coding items). But most users will find that the scoreScale()
function alone
can address their scoring needs.
scoreScale()
FunctionThe workhorse function in PROscorerTools is the scoreScale()
function. Its
basic job is to take a data frame containing responses to some items, and output
a single score for those items.
The scoreScale()
function has simple, flexible arguments that enable it to
handle nearly all scoring situations.
Features:
Reverse Coding: Before calculating a score, scoreScale()
can reverse
code all of the items, only some specific items, or none of the items (no
reverse coding is the default).
Different Types of Scores: Some instruments need to be scored by summing
item responses, others by taking the mean of item responses, and others by
re-scaling the sum or mean scores to range from 0 to 100. All 3 of these score
types are available in the scoreScale()
function.
Calculation of Scores with Missing Items: For most instruments, valid
scores can be obtained despite a certain number of missing item responses. For
example, on the EORTC QLQ-C30, a score can be calculated as long as at least 50%
of items on a given scale are non-missing. The scoreScale()
function allows
the user to specify the proportion of missing items that is allowed, and the
score is prorated to be comparable to scores with no missing items. If a
respondent has more than the allowed proportion of missing items, then that
respondent will be assigned a missing score (e.g., NA
).
Scoring Instruments with Multiple Scores: More complex instruments that
yield more than a single score can be scored by combining multiple calls to the
scoreScale()
function. In fact, most of the functions in the PROscorer
package call scoreScale()
multiple times.
Install the stable version from CRAN (recommended):
install.packages("PROscorerTools")
If you want to contribute to the development of the PROscorerTools or PROscorer packages, then you can install the development version from GitHub (generally NOT recommended):
devtools::install_github("MSKCC-Epi-Bio/PROscorerTools")
Load PROscorerTools in your R workspace:
library(PROscorerTools)
As an example, we will use the makeFakeData()
function to make a data frame of
responses to 6 fake items from 20 imaginary respondents. The created data set
(named "dat") has an "id" variable, plus responses to 6 items (named "q1", "q2",
etc.) from 20 imaginary respondents. There are also missing responses ("NA")
scattered throughout.
dat <- makeFakeData(n = 20, nitems = 6, values = 0:4, id = TRUE)
Below we use the scoreScale
function to score the fake responses in "dat". We
use the items
argument to tell scoreScale
which variables are the items we
want to score. We will score the items by summing the responses (type =
"sum"
). We will save the scores from the fake questionnaire in a data frame
named "dat_scored".
dat_scored <- scoreScale(df = dat, items = 2:7, type = "sum") dat_scored
By default, scoreScale
will score the items for a given respondent as long as
the respondent is missing no more than 50% of the items. This can be changed
with the okmiss
argument. Above, okmiss = 0.50
by default, so a respondent
could be missing 3 of the 6 items and still be assigned a score (if missing 4 or
more items, they were assigned a score of NA
). Below, we again score the
items, but this time we allow less than half of the items to be missing to be
scored (okmiss = 0.49
).
dat_scored <- scoreScale(df = dat, items = 2:7, type = "sum", okmiss = 0.49) dat_scored
By default, scoreScale
will score the items for a given respondent as long as
the respondent is missing no more than 50% of the items. This can be changed
with the okmiss
argument. Above, okmiss = 0.50
by default, so a respondent
could be missing 3 of the 6 items and still be assigned a score (if missing 4 or
more items, they were assigned a score of NA
). Below, we again score the
items, but this time we allow less than half of the items to be missing to be
scored (okmiss = 0.49
).
dat_scored <- scoreScale(df = dat, items = 2:7, type = "sum", okmiss = 0.49) dat_scored
For more information on the scoreScale
function, you can access its "help"
page by typing ?scoreScale
into R.
The PROscorer family of R packages includes PROscorer, PROscorerTools, and FACTscorer. My priorities for developing these 3 packages are:
Streamline how the packages check arguments and processes input to
scoreScale
and other custom-written scoring functions. The current system
gets the job done, but it is not very pretty. I believe that a more elegant,
easy-to-use system for performing these tasks (possibly using the
assertive package) will speed up
the expansion of the PROscorer package (which contains custom scoring functions
for specific, commonly-used PROs). I hope to have a stable version of this
system in the next major update of PROscorerTools.
Make the unit testing framework of PROscorer and PROscorerTools more comprehensive. Most of the code underlying the PROscorer functions will be already be tested by the PROscorerTools tests; however, I intend to come up with a standard set of tests for PROscorer functions to make it easier for me and others to add unit tests to their scoring functions.
Expand PROscorer with more scoring functions for specific PROs.
Finalize the collaborative infrastructure (e.g., on GitHub) by which users can use PROscorerTools to write scoring functions for their favorite PROs and submit them for inclusion in PROscorer. A major component of this is to add a few instructional vignettes, including a step-by-step guide for writing the scoring functions, guidelines for writing the instrument descriptions, and templates for writing the function documentation.
Update the FACTscorer
package. FACTscorer scores the FACT (Functional Assessment of Cancer Therapy)
and FACIT (Functional Assessment of Chronic Illness Therapy) family of measures.
Before writing PROscorerTools, I had completely re-written most of the
underlying FACTscorer code to be more foolproof and easier to update in the
future. I also wrote an "Instrument Descriptions" vignette, similar to what is
included with PROscorer. I will put the finishing touches on the FACTscorer
update and release it as soon as the above work is done.
Add capability to generate IRT-based scores for PROs that use that scoring method. I know many researchers that use various PROMIS measures. They would prefer to use the IRT-based scoring method, but find it too difficult to integrate into their research workflow. PROscorer could make IRT-based scores accessible to a much wider group of researchers.
You can access the "help" page for the PROscorerTools package by typing
?PROscorerTools
into R.
If you have any feature requests, or you want to report bugs or other strange behavior in PROscorerTools, please submit them to me on the PROscorerTools GitHub page:
Check out the PROscorerTools vignettes.
For examples on how to use the scoreScale
function within a more complex
scoring function, check out the source code for some of the functions in the
PROscorer package.
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