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Description

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>.

EScvtmle

This package implements the experiment-selector cross-validated targeted maximum likelihood estimator (ES-CVTMLE) for integrating observational and RCT data described in Dang et al. (2022). The ES-CVTMLE selects and then analyzes the experiment (RCT only or RCT with real-world data) with the best estimated bias-variance tradeoff. If a negative control outcome (NCO) is available, the bias estimate may include the estimated ATE of treatment on the NCO, which facilitates inclusion of unbiased real-world data. For more information, see:

Dang LE, Tarp JM, Abrahamsen TJ, Kvist K, Buse JB, Petersen M, van der Laan M (2022). A Cross-Validated Targeted Maximum Likelihood Estimator for Data-Adaptive Experiment Selection Applied to the Augmentation of RCT Control Arms with External Data. arXiv:2210.05802 [stat.ME]

Metadata

Version

0.0.2

License

Unknown

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