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| Title | AteMeVs: An R package for the estimation of the average treatment effects with measurement error and variable selection for confounders |
| Creator | 陳立榜 Chen, Li-Pang;Yi, Grace Y. |
| Contributor | 統計系 |
| Date | 2024-09 |
| Date Issued | 17-Jan-2025 10:50:11 (UTC+8) |
| Summary | 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. |
| Relation | PLoS ONE, Vol.19, No.9, e0296951, pp.1-20 |
| Type | article |
| DOI | https://doi.org/10.1371/journal.pone.0296951 |
| dc.contributor | 統計系 | |
| dc.creator (作者) | 陳立榜 | |
| dc.creator (作者) | Chen, Li-Pang;Yi, Grace Y. | |
| dc.date (日期) | 2024-09 | |
| dc.date.accessioned | 17-Jan-2025 10:50:11 (UTC+8) | - |
| dc.date.available | 17-Jan-2025 10:50:11 (UTC+8) | - |
| dc.date.issued (上傳時間) | 17-Jan-2025 10:50:11 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/155257 | - |
| dc.description.abstract (摘要) | 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. | |
| dc.format.extent | 108 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | PLoS ONE, Vol.19, No.9, e0296951, pp.1-20 | |
| dc.title (題名) | AteMeVs: An R package for the estimation of the average treatment effects with measurement error and variable selection for confounders | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1371/journal.pone.0296951 | |
| dc.doi.uri (DOI) | https://doi.org/10.1371/journal.pone.0296951 |