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?

Last updated 1 years ago

4.00 score 6 scripts 174 downloads

metabias - Meta-Analysis for Within-Study and/or Across-Study Biases

Provides common components (classes, methods, documentation) for packages that conduct meta-analytic corrections and sensitivity analyses for within-study and/or across-study biases in meta-analysis. See the packages 'PublicationBias', 'phacking', and 'multibiasmeta'. These package implement methods described in, respectively: Mathur & VanderWeele (2020) <doi:10.31219/osf.io/s9dp6>; Mathur (2022) <doi:10.31219/osf.io/ezjsx>; Mathur (2022) <doi:10.31219/osf.io/u7vcb>.

Last updated 1 years ago

3.65 score 3 packages 266 downloads

PublicationBias - Sensitivity Analysis for Publication Bias in Meta-Analyses

Performs sensitivity analysis for publication bias in meta-analyses (per Mathur & VanderWeele, 2020 [<doi:10.31219/osf.io/s9dp6>]). These analyses enable statements such as: "For publication bias to shift the observed point estimate to the null, 'significant' results would need to be at least 30-fold more likely to be published than negative or 'nonsignificant' results." Comparable statements can be made regarding shifting to a chosen non-null value or shifting the confidence interval. Provides a worst-case meta-analytic point estimate under maximal publication bias obtained simply by conducting a standard meta-analysis of only the negative and "nonsignificant" studies.

Last updated 1 years ago

3.52 score 11 scripts 295 downloads

truncnormbayes - Estimates Moments for a Truncated Normal Distribution using 'Stan'

Finds the posterior modes for the mean and standard deviation for a truncated normal distribution with one or two known truncation points. The method used extends Bayesian methods for parameter estimation for a singly truncated normal distribution under the Jeffreys prior (see Zhou X, Giacometti R, Fabozzi FJ, Tucker AH (2014). "Bayesian estimation of truncated data with applications to operational risk measurement". <doi:10.1080/14697688.2012.752103>). This package additionally allows for a doubly truncated normal distribution.

Last updated 1 years ago

3.18 score 2 scripts 193 downloads

phacking - Sensitivity Analysis for p-Hacking in Meta-Analyses

Fits right-truncated meta-analysis (RTMA), a bias correction for the joint effects of p-hacking (i.e., manipulation of results within studies to obtain significant, positive estimates) and traditional publication bias (i.e., the selective publication of studies with significant, positive results) in meta-analyses [see Mathur MB (2022). "Sensitivity analysis for p-hacking in meta-analyses." <doi:10.31219/osf.io/ezjsx>.]. Unlike publication bias alone, p-hacking that favors significant, positive results (termed "affirmative") can distort the distribution of affirmative results. To bias-correct results from affirmative studies would require strong assumptions on the exact nature of p-hacking. In contrast, joint p-hacking and publication bias do not distort the distribution of published nonaffirmative results when there is stringent p-hacking (e.g., investigators who hack always eventually obtain an affirmative result) or when there is stringent publication bias (e.g., nonaffirmative results from hacked studies are never published). This means that any published nonaffirmative results are from unhacked studies. Under these assumptions, RTMA involves analyzing only the published nonaffirmative results to essentially impute the full underlying distribution of all results prior to selection due to p-hacking and/or publication bias. The package also provides diagnostic plots described in Mathur (2022).

Last updated 1 years ago

3.00 score 1 stars 7 scripts 166 downloads