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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# growthcleanr
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R package for cleaning data from Electronic Health Record systems, focused on
cleaning height and weight measurements.
<a name="cite"></a> This package implements the
[Daymont et al. algorithm](https://academic.oup.com/jamia/article/24/6/1080/3767271),
as specified in Supplemental File 3 within the
[Supplementary Material](https://academic.oup.com/jamia/article/24/6/1080/3767271#97610899)
published with that paper.
> Carrie Daymont, Michelle E Ross, A Russell Localio, Alexander G Fiks, Richard
> C Wasserman, Robert W Grundmeier, Automated identification of implausible
> values in growth data from pediatric electronic health records, Journal of the
> American Medical Informatics Association, Volume 24, Issue 6, November 2017,
> Pages 1080–1087, https://doi.org/10.1093/jamia/ocx037
This package also includes an R version of the
[SAS macro published by the CDC](https://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas.htm)
for calculating percentiles and Z-scores of pediatric growth observations and
utilities for working with both functions. As of summer 2021, it also supports
cleaning anthropometric measurements for adults up to age 65. The adult
algorithm has not yet been published in a peer-reviewed publication, but is
described in detail at
[Adult algorithm](https://carriedaymont.github.io/growthcleanr/articles/adult-algorithm.html).
## Installation
To install the stable version from CRAN:
```{r, eval = FALSE}
install.packages("growthcleanr")
```
## Summary
The `growthcleanr` package processes data prepared in a specific format to
identify biologically implausible height and weight measurements. It bases these
evaluations on techniques which use patient-specific longitudinal analysis and
variations from published growth trajectory charts for pediatric subjects. These
techniques are performed in a specific order which refines and improves results
throughout the process.
Results from `growthcleanr` include a flag for each measurement indicating
whether it is to be included or excluded based on plausibility, with a variety
of specific types of exclusions identified distinctly. These flags can be
analyzed further by researchers studying anthropometric EHR data to determine
which measurements to include or exclude in their own studies. No values are
deleted or otherwise removed; each is only flagged in a new column.
To start running `growthcleanr`, an R installation with a variety of additional
packages is required, as is a growth measurement dataset prepared for use in
`growthcleanr`.
The rest of this documentation includes:
### Getting started:
- [Quickstart](https://carriedaymont.github.io/growthcleanr/articles/quickstart.html),
a brief tour of using growthcleanr, including data preparation
- [Installation](https://carriedaymont.github.io/growthcleanr/articles/installation.html),
options for installing growthcleanr, with notes on specific platforms and
source-level installation for developers
- [Usage](https://carriedaymont.github.io/growthcleanr/articles/usage.html),
examples of cleaning data, multiple options, example data
### Advanced topics:
- [Configuration options](https://carriedaymont.github.io/growthcleanr/articles/configuration.html),
changing growthcleanr operational settings
- [Understanding growthcleanr output](https://carriedaymont.github.io/growthcleanr/articles/output.html),
the exclusion types growthcleanr identifies
- [Adult algorithm](https://carriedaymont.github.io/growthcleanr/articles/adult-algorithm.html),
a detailed description of how growthcleanr assesses observations from adult
subjects
- [Computing BMI percentiles and Z-scores](https://carriedaymont.github.io/growthcleanr/articles/utilities.html),
additional functions for common data transforms and determining percentiles
and Z-scores using the CDC method
- [Working with large datasets](https://carriedaymont.github.io/growthcleanr/articles/large-data-sets.html),
notes and suggestions for running `growthcleanr` with large data sources
- [Next steps](https://carriedaymont.github.io/growthcleanr/articles/next-steps.html),
notes on potential enhancements to the pediatric and adult algorithms
- [Developer guidelines](https://carriedaymont.github.io/growthcleanr/articles/developer-guidelines.html), advice for contributors to this package, including a CRAN release checklist
## Changes
For a detailed history of released versions, see the
[Changelog](https://carriedaymont.github.io/growthcleanr/news/index.html)
or`NEWS.md`. Tagged releases, starting with 1.2.3 in January 2021, are listed
[at GitHub](https://github.com/carriedaymont/growthcleanr/releases).