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  "Title": "Meta-Analysis Methods Correcting for Publication Bias",
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  "Date": "2025-09-02",
  "Authors@R": "person(given = c(\"Robbie\", \"C.M.\"),\nfamily = \"van Aert\",\nrole = c(\"aut\", \"cre\"),\nemail = \"R.C.M.vanAert@tilburguniversity.edu\")",
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  "Description": "Provides meta-analysis methods that correct for\npublication bias and outcome reporting bias. Four methods and a\nvisual tool are currently included in the package. The\np-uniform method as described in van Assen, van Aert, and\nWicherts (2015) <doi:10.1037/met0000025> can be used for\nestimating the average effect size, testing the null hypothesis\nof no effect, and testing for publication bias using only the\nstatistically significant effect sizes of primary studies. The\nsecond method in the package is the p-uniform* method as\ndescribed in van Aert and van Assen (2023)\n<doi:10.31222/osf.io/zqjr9>. This method is an extension of the\np-uniform method that allows for estimation of the average\neffect size and the between-study variance in a meta-analysis,\nand uses both the statistically significant and nonsignificant\neffect sizes. The third method in the package is the hybrid\nmethod as described in van Aert and van Assen (2018)\n<doi:10.3758/s13428-017-0967-6>. The hybrid method is a\nmeta-analysis method for combining a conventional study and\nreplication/preregistered study while taking into account\nstatistical significance of the conventional study. This method\nwas extended in van Aert (2025) <doi:10.1037/met0000719> such\nthat it allows for the inclusion of multiple conventional and\nreplication/preregistered studies. The p-uniform and hybrid\nmethod are based on the statistical theory that the\ndistribution of p-values is uniform conditional on the\npopulation effect size. The fourth method in the package is the\nSnapshot Bayesian Hybrid Meta-Analysis Method as described in\nvan Aert and van Assen (2018)\n<doi:10.1371/journal.pone.0175302>. This method computes\nposterior probabilities for four true effect sizes (no, small,\nmedium, and large) based on an original study and replication\nwhile taking into account publication bias in the original\nstudy. The method can also be used for computing the required\nsample size of the replication akin to power analysis in\nnull-hypothesis significance testing. The meta-plot is a visual\ntool for meta-analysis that provides information on the primary\nstudies in the meta-analysis, the results of the meta-analysis,\nand characteristics of the research on the effect under study\n(van Assen et al., 2023). Helper functions to apply the\nCorrecting for Outcome Reporting Bias (CORB) method to correct\nfor outcome reporting bias in a meta-analysis (van Aert &\nWicherts, 2023).",
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  "Date/Publication": "2025-09-02 14:02:46 UTC",
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