multibiasmeta-package {multibiasmeta} | R Documentation |
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?
Maintainer: Peter Solymos peter@analythium.io (ORCID) [contributor]
Authors:
Maya Mathur mmathur@stanford.edu
Mika Braginsky mika.br@gmail.com
Mathur MB (2022). “Sensitivity analysis for the interactive effects of internal bias and publication bias in meta-analyses.” doi:10.31219/osf.io/u7vcb.
Ding P, VanderWeele TJ (2016). “Sensitivity analysis without assumptions.” Epidemiology (Cambridge, Mass.), 27(3), 368.
Smith LH, VanderWeele TJ (2019). “Bounding bias due to selection.” Epidemiology (Cambridge, Mass.), 30(4), 509.
VanderWeele TJ, Li Y (2019). “Simple sensitivity analysis for differential measurement error.” American journal of epidemiology, 188(10), 1823–1829.
Mathur MB, Peacock J, Reichling DB, Nadler J, Bain PA, Gardner CD, Robinson TN (2021). “Interventions to reduce meat consumption by appealing to animal welfare: Meta-analysis and evidence-based recommendations.” Appetite, 164, 105277.
Useful links:
Report bugs at https://github.com/mathurlabstanford/multibiasmeta/issues