Package: multibiasmeta 0.2.2

Peter Solymos

multibiasmeta: Sensitivity Analysis for Multiple Biases in Meta-Analyses

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?

Authors:Maya Mathur [aut], Mika Braginsky [aut], Peter Solymos [cre, ctb]

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multibiasmeta.pdf |multibiasmeta.html
multibiasmeta/json (API)
NEWS

# Install 'multibiasmeta' in R:
install.packages('multibiasmeta', repos = c('https://mathurlabstanford.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mathurlabstanford/multibiasmeta/issues

Datasets:
  • meta_meat - Meta-analysis about meat consumption

On CRAN:

2 exports 1.02 score 48 dependencies 6 scripts 186 downloads

Last updated 1 years agofrom:64e328a7fa. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 15 2024
R-4.5-winOKAug 15 2024
R-4.5-linuxOKAug 15 2024
R-4.4-winOKAug 15 2024
R-4.4-macOKAug 15 2024
R-4.3-winOKAug 15 2024
R-4.3-macOKAug 15 2024

Exports:multibias_evaluemultibias_meta

Dependencies:bootclicolorspacecpp11dplyrEValuefansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSmathjaxrMatrixmetabiasmetadatmetaforMetaUtilitymgcvmunsellnlmenumDerivpbapplypillarpkgconfigpurrrR6rbibutilsRColorBrewerRdpackrlangrobumetascalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

Metabias packages tutorial

Rendered fromtutorial.Rmdusingknitr::rmarkdownon Aug 15 2024.

Last update: 2023-08-21
Started: 2023-01-13