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. 2024 Sep 30;19(9):e0296951.
doi: 10.1371/journal.pone.0296951. eCollection 2024.

AteMeVs: An R package for the estimation of the average treatment effect with measurement error and variable selection for confounders

Li-Pang Chen  1 , Grace Y Yi  2
Affiliations

AteMeVs: An R package for the estimation of the average treatment effect with measurement error and variable selection for confounders

Li-Pang Chen et al. PLoS One. .

Abstract

In causal inference, the estimation of the average treatment effect is often of interest. For example, in cancer research, an interesting question is to assess the effects of the chemotherapy treatment on cancer, with the information of gene expressions taken into account. Two crucial challenges in this analysis involve addressing measurement error in gene expressions and handling noninformative gene expressions. While analytical methods have been developed to address those challenges, no user-friendly computational software packages seem to be available to implement those methods. To close this gap, we develop an R package, called AteMeVs, to estimate the average treatment effect using the inverse-probability-weighting estimation method to handle data with both measurement error and spurious variables. This developed package accommodates the method proposed by Yi and Chen (2023) as a special case, and further extends its application to a broader scope. The usage of the developed R package is illustrated by applying it to analyze a cancer dataset with information of gene expressions.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. An illustrative diagram of the causal relationship with possible confounders.
See this image and copyright information in PMC

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