EValue: Sensitivity Analyses for Unmeasured Confounding and Other Biases
in Observational Studies and Meta-Analyses
Conducts sensitivity analyses for unmeasured confounding, selection bias, and measurement error (individually or in combination; VanderWeele & Ding (2017) <doi:10.7326/M16-2607>; Smith & VanderWeele (2019) <doi:10.1097/EDE.0000000000001032>; VanderWeele & Li (2019) <doi:10.1093/aje/kwz133>; Smith & VanderWeele (2021) <doi:10.48550/arXiv.2005.02908>). Also conducts sensitivity analyses for unmeasured confounding in meta-analyses (Mathur & VanderWeele (2020a) <doi:10.1080/01621459.2018.1529598>; Mathur & VanderWeele (2020b) <doi:10.1097/EDE.0000000000001180>) and for additive measures of effect modification (Mathur et al., under review).
| Version: |
4.1.3 |
| Imports: |
stats, graphics, ggplot2 (≥ 2.2.1), metafor, metadat, methods, boot, MetaUtility, dplyr |
| Suggests: |
testthat, knitr, rmarkdown |
| Published: |
2021-10-28 |
| DOI: |
10.32614/CRAN.package.EValue |
| Author: |
Maya B. Mathur [cre, aut],
Louisa H. Smith [aut],
Peng Ding [aut],
Tyler J. VanderWeele [aut] |
| Maintainer: |
Maya B. Mathur <mmathur at stanford.edu> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| Citation: |
EValue citation info |
| Materials: |
README |
| In views: |
CausalInference, MetaAnalysis |
| CRAN checks: |
EValue results |
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