Meta-analyses can be compromised by studies' internal biases (e.g., confounding in nonrandomized studies) as well as by publication bias. This package conducts sensitivity analyses for the joint effects of these biases (per Mathur (2022) <doi:10.31219/osf.io/u7vcb>). These sensitivity analyses address two questions: (1) For a given severity of internal bias across studies and of publication bias, how much could the results change?; and (2) For a given severity of publication bias, how severe would internal bias have to be, hypothetically, to attenuate the results to the null or by a given amount?
Version: 0.2.2 Depends: R (≥ 4.1.0) Imports: dplyr, EValue, metabias, metafor, purrr, Rdpack, rlang, robumeta Suggests: glue, knitr, phacking, PublicationBias (≥ 2.3.0), rmarkdown, testthat (≥ 3.0.0) Published: 2023-08-23 DOI: 10.32614/CRAN.package.multibiasmeta Author: Maya Mathur [aut], Mika Braginsky [aut], Peter Solymos [cre, ctb] Maintainer: Peter Solymos <peter at analythium.io> BugReports: https://github.com/mathurlabstanford/multibiasmeta/issues License: MIT + file LICENSE URL: https://github.com/mathurlabstanford/multibiasmeta, https://mathurlabstanford.github.io/multibiasmeta/ NeedsCompilation: no Materials: README, NEWS In views: MetaAnalysis CRAN checks: multibiasmeta results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=multibiasmeta to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4