Package: EScvtmle 0.0.2

Lauren Eyler Dang

EScvtmle: Experiment-Selector CV-TMLE for Integration of Observational and RCT Data

The experiment selector cross-validated targeted maximum likelihood estimator (ES-CVTMLE) aims to select the experiment that optimizes the bias-variance tradeoff for estimating a causal average treatment effect (ATE) where different experiments may include a randomized controlled trial (RCT) alone or an RCT combined with real-world data. Using cross-validation, the ES-CVTMLE separates the selection of the optimal experiment from the estimation of the ATE for the chosen experiment. The estimated bias term in the selector is a function of the difference in conditional mean outcome under control for the RCT compared to the combined experiment. In order to help include truly unbiased external data in the analysis, the estimated average treatment effect on a negative control outcome may be added to the bias term in the selector. For more details about this method, please see Dang et al. (2022) <arxiv:2210.05802>.

Authors:Lauren Eyler Dang [cre, aut], Maya Petersen [aut], Mark van der Laan [aut]

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Bug tracker:https://github.com/lauren-eylerdang/escvtmle/issues

On CRAN:

3 exports 0.62 score 57 dependencies 3 scripts 129 downloads

Last updated 2 years agofrom:9284c2c144. Checks:OK: 7. Indexed: yes.

Exports:ES.cvtmleplot.EScvtmleprint.EScvtmle

Dependencies:abindassertthatbitopscaToolsclicodetoolscolorspacecvAUCdata.tabledigestdplyrfansifarverforeachfuturefuture.applygamgenericsggplot2globalsgluegplotsgridExtragtablegtoolsisobanditeratorsKernSmoothlabelinglatticelifecyclelistenvmagrittrMASSMatrixmgcvmunsellnlmennlsorigamiparallellypillarpkgconfigR6RColorBrewerrlangROCRscalesstringistringrSuperLearnertibbletidyselectutf8vctrsviridisLitewithr