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TriMatch

Propensity Score Matching for Non-Binary Treatments

https://github.com/jbryer/trimatch

Science Score: 36.0%

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Last synced: 10 months ago · JSON representation

Repository

Propensity Score Matching for Non-Binary Treatments

Basic Info
Statistics
  • Stars: 13
  • Watchers: 2
  • Forks: 3
  • Open Issues: 2
  • Releases: 0
Created over 13 years ago · Last pushed over 1 year ago
Metadata Files
Readme Changelog

README.Rmd 

---
output: github_document
editor_options: 
  chunk_output_type: console
---


##  TriMatch - Propensity score matching for non-binary treatments.


`r badger::badge_cran_release("TriMatch", "orange")`
`r badger::badge_devel("jbryer/TriMatch", "blue")`
`r badger::badge_github_actions("jbryer/TriMatch", action = "R-CMD-check")`
![Downloads](https://cranlogs.r-pkg.org/badges/TriMatch)
![Total Downloads](https://cranlogs.r-pkg.org/badges/grand-total/TriMatch)



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

### Abstract

The use of propensity score methods (Rosenbaum and Rubin, 1983) have become popular for estimating causal inferences in observational studies in medical research (Austin, 2008) and in the social sciences (Thoemmes and Kim, 2011). In most cases however, the use of propensity score methods have been confined to a single treatment. Several researchers have suggested using propensity score methods with multiple control groups, or to simply perform two separate analyses, one between treatment one and the control and another between treatment two and control. This talk introduces the `TriMatch` package for R that provides a method for determining matched triplets. Examples from educational and medical contexts will be discussed.

Consider two treatments, T r1 and T r2, and a control, C. We estimate propensity scores with three separate logistic regression models where model one predicts T r1 with C, model two predicts T r2 with C, and model three predicts T r1 with T r2. The triangle plot in Figure 1 represents the fitted values (i.e. propensity scores) from the three models on each edge. Since each unit has a propensity score in two models, their scores are connected. The `TriMatch` algorithm will find matched triplets where the sum of the distances within each model is minimized. In Figure 1, the black lines illustrate one matched triplet.

Propensity score analysis of two groups typically use dependent sample t-tests. The analogue for matched triplets include
Figure 1: Triangle Plot repeated measures ANOVA and the Freidman Rank Sum Test. The `TriMatch` package provides utility functions for conducting and visualizing these statistical tests. Moreover, a set of functions extending PSAgraphics (Helmreich and Pruzek, 2009) for matched triplets to check covariate balance are provided.

```{r, echo=FALSE, out.width='80%', fig.align='center'}
knitr::include_graphics('man/figures/matches.png')
```

### References

Austin, P. (2008). A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. *Statistics in Medicine 27*, 2037–2049.

Helmreich, J. E. and R. M. Pruzek (2009, 2). Psagraphics: An r package to support propensity score analysis. *Journal of Statistical Software 29*(6), 1–23.

Rosenbaum, P. R. and D. B. Rubin (1983). The central role of the propensity score in observational studies for causal effects. *Biometrika 70*, 41–55.

Thoemmes, F. J. and E. S. Kim (2011). A systematic review of propensity score methods in the social sciences. *Multivariate Behavioral Research 46*, 90–118.


### Keywords

propensity score analysis, matching, non-binary treatments

### Installation

```{r, eval=FALSE}
# Install from CRAN
install.packages('TriMatch')

# Or install the package from Github
remotes::install_github('TriMatch', 'jbryer')
```

See `vignette('TriMatch')` for more details. See the [Applied Propensity Score Analysis with R](https://psa.bryer.org) book and R package for a general introduction to propensity score methods.

Owner

  • Name: Jason Bryer
  • Login: jbryer
  • Kind: user
  • Location: Albany, NY
  • Company: City University of New York

Assistant Professor and Associate Director, Data Science and Information Systems, School of Professional Studies, CUNY

GitHub Events

Total
  • Push event: 7
Last Year
  • Push event: 7

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 72
  • Total Committers: 2
  • Avg Commits per committer: 36.0
  • Development Distribution Score (DDS): 0.153
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Jason Bryer j****n@b****g 61
Jason Bryer j****r@a****u 11
Committer Domains (Top 20 + Academic)
albany.edu: 1 bryer.org: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 3
  • Total pull requests: 1
  • Average time to close issues: about 1 year
  • Average time to close pull requests: N/A
  • Total issue authors: 3
  • Total pull request authors: 1
  • Average comments per issue: 0.33
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • xbaibs (1)
  • hadley (1)
  • ykarayankov (1)
Pull Request Authors
  • ppanko (1)
Top Labels
Issue Labels
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 363 last-month
  • Total dependent packages: 1
  • Total dependent repositories: 2
  • Total versions: 7
  • Total maintainers: 1
cran.r-project.org: TriMatch

Propensity Score Matching of Non-Binary Treatments

  • Versions: 7
  • Dependent Packages: 1
  • Dependent Repositories: 2
  • Downloads: 363 Last month
Rankings
Forks count: 14.2%
Stargazers count: 15.1%
Dependent packages count: 18.1%
Dependent repos count: 19.3%
Average: 22.0%
Downloads: 43.4%
Maintainers (1)
jason@bryer.org
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.0 depends
  • ez * depends
  • ggplot2 * depends
  • reshape2 * depends
  • scales * depends
  • PSAgraphics * imports
  • compiler * imports
  • grid * imports
  • gridExtra * imports
  • psych * imports
  • randomForest * imports
  • stats * imports
  • MASS * suggests
  • xtable * suggests
.github/workflows/R-CMD-check.yaml actions
  • actions/checkout v4 composite
  • r-lib/actions/check-r-package v2 composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-r-dependencies v2 composite
  • r-lib/actions/setup-tinytex v2 composite