It has been well documented that ignoring measurement error may result in severely biased inference results. In recent years, there has been limited but increasing research on causal inference with measurement error. In the presence of misclassified binary outcome variable, Shu and Yi (2017) considered the inverse probability weighted estimation of the average treatment effect and proposed valid estimation methods to correct for misclassification effects for various settings. To expedite the application of those methods for situations where misclassification in the binary outcome variable is a real concern, we implement correction methods proposed by Shu and Yi (2017) and develop an R package ipwErrorY for general users. Simulated datasets are used to illustrate the use of the developed package.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2019-029.zip
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For attribution, please cite this work as
Shu & Yi, "The R Journal: ipwErrorY: An R Package for Estimation of Average Treatment Effect with Misclassified Binary Outcome", The R Journal, 2019
BibTeX citation
@article{RJ-2019-029,
author = {Shu, Di and Yi, Grace Y.},
title = {The R Journal: ipwErrorY: An R Package for Estimation of Average Treatment Effect with Misclassified Binary Outcome},
journal = {The R Journal},
year = {2019},
note = {https://doi.org/10.32614/RJ-2019-029},
doi = {10.32614/RJ-2019-029},
volume = {11},
issue = {1},
issn = {2073-4859},
pages = {337-351}
}