
WVIECHTB . GITHUB . IO {
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Title:
metafor: A Meta-Analysis Package for R — metafor-package • metafor
Description:
The metafor package provides a comprehensive collection of functions for conducting meta-analyses in R. The package can be used to calculate a wide variety of effect sizes or outcome measures and allows the user to fit equal-, fixed-, and random-effects models to these data. By including study-level variables (‘moderators’) as predictors in these models, (mixed-effects) meta-regression models can also be fitted. For meta-analyses of \(2 \times 2\) tables, proportions, incidence rates, and incidence rate ratios, the package also provides specialized methods, including the Mantel-Haenszel method, Peto
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Keywords {🔍}
model, package, models, function, true, effectsoutcomes, ratios, method, data, functions, effect, studies, mixedeffects, sizes, outcome, metafor, measures, incidence, escalc, metaanalytic, metaanalyses, variables, sampling, differences, rmauni, denotes, multiple, metaanalysis, variety, randomeffects, errors, outcomes, standard, observed, effectoutcome, rmamh, rmapeto, rmaglmm, rmamv, conducting, fit, equal, including, metaregression, rate, methods, andor, standardized, average, coefficients,
Topics {✒️}
yi=β0+β1xi1+β2xi2+…+βp′xip′+ui+εi mixed-effects meta-regression model θ1=θ2=…=θk≡θ random/mixed-effects models standard ‘inverse-variance’ method yi=μ+ui+εi mixed-effects model denotes called ‘meta-regression’ models average true effect/outcome multiple effects/outcomes derived common control/reference group including study-level variables term ‘fixed-effects model fitting equal-effects models mixed-effects logistic meta-analytic models yi=θ+εi meta-analytic literature meta-regression models poisson regression models random-effects models effects/outcomes derived control/reference group �poisson-normal’ models obtaining confidence intervals normal quantile-quantile central hypergeometric distribution shared phylogenetic history good starting place russell sage foundation national cancer institute meta-regression model assess model fit study-level variables true effects/outcomes multiple treatment groups effects/outcomes tend conducting meta-analyses network meta-analyses multiple treatment studies correlated true effects true effect/outcome random-effects model �small-study effects jth moderator variable conducting sensitivity analyses variance stabilizing transformation �normal-normal’ model equal-effects model independent effects/outcomes
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