README.Rmd

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# PROscorerTools

## Overview

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.

## The `scoreScale()` Function

The 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.



## Installation and Basic Usage

Install the stable version from CRAN (recommended):

```{r eval=FALSE}
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):

```{r eval=FALSE}
devtools::install_github("MSKCC-Epi-Bio/PROscorerTools")
```

Load PROscorerTools in your R workspace:

```{r eval = FALSE}
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.

```{r eval = FALSE}
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".

```{r eval=FALSE}
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`).

```{r eval=FALSE}
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.



## Resources for More Information

* You can access the "help" page for "PROscorerTools" package by typing
`?PROscorerTools` into R.

* Check out the [PROscorerTools
vignettes](https://CRAN.R-project.org/package=PROscorerTools).

* 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.