RCTrep: Validation of Estimates of Treatment Effects in Observational
Data
Validates estimates of (conditional) average treatment effects obtained using observational data by a) making it easy to obtain and visualize estimates derived using a large variety of methods (G-computation, inverse propensity score weighting, etc.), and b) ensuring that estimates are easily compared to a gold standard (i.e., estimates derived from randomized controlled trials). 'RCTrep' offers a generic protocol for treatment effect validation based on four simple steps, namely, set-selection, estimation, diagnosis, and validation. 'RCTrep' provides a simple dashboard to review the obtained results. The validation approach is introduced by Shen, L., Geleijnse, G. and Kaptein, M. (2023) <doi:10.21203/rs.3.rs-2559287/v2>.
| Version: |
1.2.0 |
| Depends: |
R (≥ 2.10), base |
| Imports: |
mvtnorm, MatchIt, ggplot2, ggpubr, PSweight, numDeriv, R6, dplyr, geex, BART, fastDummies, tidyr, copula, shiny, shinydashboard, glue, stats, utils, caret |
| Suggests: |
rmarkdown, knitr, testthat (≥ 3.0.0) |
| Published: |
2023-11-02 |
| DOI: |
10.32614/CRAN.package.RCTrep |
| Author: |
Lingjie Shen [aut, cre, cph],
Gijs Geleijnse [aut],
Maurits Kaptein [aut] |
| Maintainer: |
Lingjie Shen <lingjieshen66 at gmail.com> |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/duolajiang/RCTrep |
| NeedsCompilation: |
no |
| Citation: |
RCTrep citation info |
| Materials: |
README NEWS |
| CRAN checks: |
RCTrep results |
Documentation:
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