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>.
Package details |
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Author | Lingjie Shen [aut, cre, cph], Gijs Geleijnse [aut], Maurits Kaptein [aut] |
Maintainer | Lingjie Shen <lingjieshen66@gmail.com> |
License | MIT + file LICENSE |
Version | 1.2.0 |
URL | https://github.com/duolajiang/RCTrep |
Package repository | View on CRAN |
Installation |
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