Sensitivity Analyses for Unmeasured Confounding in Meta-Analyses

Conducts sensitivity analyses for unmeasured confounding in random-effects meta-analysis per Mathur & VanderWeele (in preparation). Given output from a random-effects meta-analysis with a relative risk outcome, computes point estimates and inference for: (1) the proportion of studies with true causal effect sizes more extreme than a specified threshold of scientific significance; and (2) the minimum bias factor and confounding strength required to reduce to less than a specified threshold the proportion of studies with true effect sizes of scientifically significant size. Creates plots and tables for visualizing these metrics across a range of bias values. Provides tools to easily scrape study-level data from a published forest plot or summary table to obtain the needed estimates when these are not reported.


Reference manual

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1.3.0 by Maya B. Mathur, a month ago

Browse source code at

Authors: Maya B. Mathur, Tyler J. VanderWeele

Documentation:   PDF Manual  

Task views: Meta-Analysis

GPL-2 license

Imports ggplot2, metafor, stats

See at CRAN