Author: František Bartoš

# Publication-Bias Adjustment

#### František Bartoš

#### 28th of April 2026

This vignette illustrates how to use the `RoBMA` R package (Bartoš & Maier, 2020) for single-model publication-bias adjustment. We walk through the ego-depletion dataset from the `metadat` package, fitting three single-model bias adjustments alongside their `metafor` package counterparts (Viechtbauer, 2010): PET and PEESE (Stanley & Doucouliagos, 2014), and a weight-function selection model (Vevea & Hedges, 1995). All of the models can also be combined with Bayesian model-averaging into a Robust Bayesian meta-analysis as described in the Robust Bayesian Meta-Analysis vignette.

The vignette is organized in three parts. First, we describe the default prior distributions on the bias parameters. Second, we fit each model with its default prior distribution, check parity against the `metafor` package, and contrast the implied funnels. Third, we relax each default in turn, placing the restricted and relaxed posteriors side by side.

For the non-bias-adjusted workflow showing more features (also applicable to the bias-adjustment models) see the Bayesian Meta-Analysis vignette. The Prior Distributions vignette covers setting prior distributions for the meta-analytic parameters.

## Default Bias-Adjustment Prior Distributions

The three model types use a separate prior distribution on the bias-adjustment parameter, independent of the prior distributions on `mu`, `tau`, and the meta-regression coefficients.

Model | Bias parameter | Default prior distribution
--- | --- | ---
`bPET()` | `PET` | Cauchy(\(0\), \(1\)), truncated to \([0, \infty)\)
`bPEESE()` | `PEESE` | Cauchy(\(0\), \(5 \cdot \text{UISD}_{\text{ratio}}\)), truncated to \([0, \infty)\)
`bselmodel()` | `omega` | one-sided weight function with `steps = c(0.025)` and `alpha = c(1, 1)`

The PET prior distribution is invariant to the effect-size measure. The PEESE scale is rescaled by \(\text{UISD}_{\text{ratio}} = \text{UISD}_{\text{SMD}} / \text{UISD}_{\text{measure}}\) so the prior distribution describes a comparable bias on each scale; the ratio is \(1\) for `measure = "SMD"`. The weight-function default specifies a Dirichlet prior distribution on the relative publication probabilities of one-sided p-values below and above \(0.025\), equivalent to a three-parameter selection model (3PSM). Unlike the prior distributions for meta-analytic parameters, the bias-adjustment prior distributions are not affected by `rescale_priors`; they can be replaced via `prior_bias`, and the weight-function `steps` can be changed directly via the `steps` argument. The bias-adjustment models rely on the `effect_direction` argument. Selection models penalize one-sided p-values evaluated against the expected sign of the effect, and the truncated PET and PEESE prior distributions constrain the small-study coefficient to pull the estimate toward zero from the dominant direction. The `effect_direction` argument tells the model which side that is. The default `effect_direction = "detect"` infers the direction from the median of `yi` (positive median yields `"positive"`, negative median yields `"negative"`). Set `effect_direction = "positive"` or `effect_direction = "negative"` explicitly when the dominant direction is known a priori.

These prior distributions correspond to the default settings tested in the larger RoBMA-PSMA ensemble in Bartoš et al. (2023). Note that all three defaults are restricted to assume the presence of publication bias: the truncated PET / PEESE coefficients are constrained to pull the estimate toward zero, and the cumulative-Dirichlet weight function forces every non-significant interval’s `omega` to be no greater than the most-significant interval’s reference value of \(1\). The “no publication bias” case is therefore on the parameter-space boundary.

In a model-averaged `RoBMA()` ensemble this is exactly the desired behaviour: a separate no-bias model carries the “no selection” hypothesis, and the bias-adjustment models are deliberately one-sided so the contrast against the no-bias spike is sharp and the inclusion Bayes factor is diagnostic. For the single-model fits in this vignette there is no companion no-bias model in the ensemble, so the same restriction can shrink estimates toward the implied bias even when the data are consistent with no selection. The Flexible Bias Prior Distributions section below relaxes the restriction model by model via the `prior_bias` argument when the single-model fit is the analysis of interest.

## Default Fits

The `dat.lehmann2018` dataset from the `metadat` package collects 81 standardized mean differences from the ego-depletion meta-analysis. Effect sizes `yi` and sampling variances `vi` are already provided.

    data("dat.lehmann2018", package = "metadat")
    dat <- dat.lehmann2018

We fit each of the three bias-adjustment models with its default prior distribution and contrast against the matching `metafor` package call. The funnel-plot view at the end of the section then visualizes how each adjustment encodes a different bias mechanism.

### PET

The PET model regresses effect sizes on the standard error. With `metafor::rma()` it is fit by passing `mods = ~ sqrt(vi)`; the slope on `sqrt(vi)` is the PET coefficient. The `RoBMA` R package implements PET as a meta-regression (with a random-effects heterogeneity component) rather than the UWLS variant, for consistency with the rest of the framework.

    fit_PET_metafor <- metafor::rma(yi, vi, mods = ~ sqrt(vi), data = dat)
    fit_PET_metafor
    #>
    #> Mixed-Effects Model (k = 81; tau^2 estimator: REML)
    #>
    #> tau^2 (estimated amount of residual heterogeneity):     0.0940 (SE = 0.0236)
    #> tau (square root of estimated tau^2 value):             0.3065
    #> I^2 (residual heterogeneity / unaccounted variability): 79.74%
    #> H^2 (unaccounted variability / sampling variability):   4.94
    #> R^2 (amount of heterogeneity accounted for):            8.96%
    #>
    #> Test for Residual Heterogeneity:
    #> QE(df = 79) = 234.7716, p-val < .0001
    #>
    #> Test of Moderators (coefficient 2):
    #> QM(df = 1) = 6.5617, p-val = 0.0104
    #>
    #> Model Results:
    #>
    #>           estimate      se     zval    pval    ci.lb   ci.ub
    #> intrcpt    -0.0359  0.1038  -0.3455  0.7297  -0.2394  0.1676
    #> sqrt(vi)    1.0765  0.4203   2.5616  0.0104   0.2528  1.9002  *
    #>
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

`bPET()` fits the same model with the default Cauchy prior distribution on the (positive) PET coefficient. `measure = "SMD"` selects the default prior distributions for standardized mean differences.

    fit_PET_brma <- bPET(
      yi = yi, vi = vi, measure = "SMD",
      data = dat, seed = 1
    )

    summary(fit_PET_brma, include_mcmc_diagnostics = FALSE)
    #>
    #> Bayesian Random-Effects PET Model (k = 81)
    #>
    #> Estimates
    #>       Mean    SD  0.025    0.5 0.975
    #> mu  -0.004 0.097 -0.196 -0.002 0.181
    #> tau  0.307 0.040  0.233  0.305 0.391
    #>
    #> Publication Bias
    #>      Mean    SD 0.025   0.5 0.975
    #> PET 0.932 0.386 0.214 0.924 1.699

The posterior mean of `mu` is the bias-corrected average effect; the `PET` coefficient quantifies how strongly small studies pull the meta-analytic mean. The left panel shows the prior and posterior of the `PET` coefficient with `prior = TRUE` overlaying the prior distribution; the right panel shows the implied PET regression of effect on standard error from `plot_pet_peese()`, with the prior band overlaid.

    par(mfrow = c(1, 2), mar = c(4, 4, 2, 1), cex.axis = 0.8, cex.lab = 0.8, cex.main = 0.75)
    plot(fit_PET_brma,  parameter = "PET", prior = TRUE, main = "PET coefficient", xlim = c(0, 4))
    plot_pet_peese(fit_PET_brma, prior = TRUE, main = "PET regression")

The half-Cauchy prior distribution on `PET` is wide; the posterior concentrates near \(1\), broadly consistent with the `metafor` package estimate but pulled toward zero by the prior distribution. The implied regression line intersects the distribution of effect sizes and standard errors.

### PEESE

The PEESE model regresses effect sizes on the sampling variance instead of the standard error. With `metafor::rma()` it is fit by passing `mods = ~ vi`; the slope on `vi` is the PEESE coefficient. As with PET, the `RoBMA` R package PEESE is the meta-regression version (retaining the heterogeneity component), not the UWLS variant.

    fit_PEESE_metafor <- metafor::rma(yi, vi, mods = ~ vi, data = dat)
    fit_PEESE_metafor
    #>
    #> Mixed-Effects Model (k = 81; tau^2 estimator: REML)
    #>
    #> tau^2 (estimated amount of residual heterogeneity):     0.0917 (SE = 0.0232)
    #> tau (square root of estimated tau^2 value):             0.3028
    #> I^2 (residual heterogeneity / unaccounted variability): 79.77%
    #> H^2 (unaccounted variability / sampling variability):   4.94
    #> R^2 (amount of heterogeneity accounted for):            11.18%
    #>
    #> Test for Residual Heterogeneity:
    #> QE(df = 79) = 231.8504, p-val < .0001
    #>
    #> Test of Moderators (coefficient 2):
    #> QM(df = 1) = 5.6925, p-val = 0.0170
    #>
    #> Model Results:
    #>
    #>          estimate      se    zval    pval    ci.lb   ci.ub
    #> intrcpt    0.0903  0.0647  1.3959  0.1627  -0.0365  0.2171
    #> vi         1.8824  0.7890  2.3859  0.0170   0.3360  3.4287  *
    #>
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

`bPEESE()` fits the same model with the default Cauchy prior distribution on the (positive) PEESE coefficient, with the prior scale rescaled to the effect-size measure.

    fit_PEESE_brma <- bPEESE(
      yi = yi, vi = vi, measure = "SMD",
      data = dat, seed = 1
    )

    summary(fit_PEESE_brma, include_mcmc_diagnostics = FALSE)
    #>
    #> Bayesian Random-Effects PEESE Model (k = 81)
    #>
    #> Estimates
    #>      Mean    SD  0.025   0.5 0.975
    #> mu  0.092 0.063 -0.031 0.092 0.216
    #> tau 0.304 0.041  0.230 0.302 0.389
    #>
    #> Publication Bias
    #>        Mean    SD 0.025   0.5 0.975
    #> PEESE 1.840 0.759 0.376 1.837 3.350

As for PET, the left panel shows the prior and posterior of the `PEESE` coefficient and the right panel shows the implied PEESE regression of effect on sampling variance.

    par(mfrow = c(1, 2), mar = c(4, 4, 2, 1), cex.axis = 0.8, cex.lab = 0.8, cex.main = 0.75)
    plot(fit_PEESE_brma, parameter = "PEESE", prior = TRUE, main = "PEESE coefficient", xlim = c(0, 8))
    plot_pet_peese(fit_PEESE_brma, prior = TRUE, main = "PEESE regression")

The bias-corrected effect under PEESE is also closer to zero than the random-effects estimate due to the weakly informative prior distributions.

### Weight Function Selection Model

The selection model multiplies the likelihood of each study by a p-value-dependent publication probability. With the `metafor` package, a one-sided three-parameter model is fit by passing the random-effects fit to `metafor::selmodel()`.

    fit_rma_metafor      <- metafor::rma(yi, vi, data = dat, method = "ML")
    fit_selmodel_metafor <- metafor::selmodel(fit_rma_metafor, type = "stepfun", steps = .025, decreasing = TRUE)
    fit_selmodel_metafor
    #>
    #> Random-Effects Model (k = 81; tau^2 estimator: ML)
    #>
    #> tau^2 (estimated amount of total heterogeneity): 0.0811 (SE = 0.0261)
    #> tau (square root of estimated tau^2 value):      0.2848
    #>
    #> Test for Heterogeneity:
    #> LRT(df = 1) = 37.3674, p-val < .0001
    #>
    #> Model Results:
    #>
    #> estimate      se    zval    pval   ci.lb   ci.ub
    #>   0.1328  0.0655  2.0267  0.0427  0.0044  0.2612  *
    #>
    #> Test for Selection Model Parameters:
    #> LRT(df = 1) = 1.7646, p-val = 0.1840
    #>
    #> Selection Model Results:
    #>
    #>                      k  estimate      se     zval    pval   ci.lb   ci.ub
    #> 0     < p <= 0.025  25    1.0000     ---      ---     ---     ---     ---
    #> 0.025 < p <= 1      56    0.5485  0.2495  -1.8097  0.0703  0.0594  1.0000  .
    #>
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

`bselmodel()` fits the same model in one call. The default weight function uses a single one-sided cutoff at \(0.025\). Passing `steps = c(0.025, 0.5)` (or any other vector) specifies a finer step function; the prior distribution on the omega vector is uniform across the implied intervals. The default Dirichlet prior distribution on `omega` in the `RoBMA` R package enforces a monotonically decreasing weight function (non-significant intervals cannot exceed the reference interval), which matches `metafor::selmodel(..., decreasing = TRUE)` above.

    fit_selmodel_brma <- bselmodel(
      yi = yi, vi = vi, measure = "SMD",
      data = dat, seed = 1
    )

    summary(fit_selmodel_brma, include_mcmc_diagnostics = FALSE)
    #>
    #> Bayesian Random-Effects Selection Model (k = 81)
    #>
    #> Estimates
    #>      Mean    SD 0.025   0.5 0.975
    #> mu  0.141 0.057 0.029 0.142 0.252
    #> tau 0.293 0.043 0.212 0.292 0.380
    #>
    #> Publication Bias
    #>                 Mean    SD 0.025   0.5 0.975
    #> omega[0,0.025] 1.000 0.000 1.000 1.000 1.000
    #> omega[0.025,1] 0.621 0.192 0.273 0.614 0.969
    #> P-value intervals for publication bias weights omega correspond to one-sided p-values.

The omega vector is reported with `omega[0, 0.025]` fixed to \(1\) as the reference interval; `omega[0.025, 1]` is the publication probability of one-sided p-values above \(0.025\) relative to the significant interval. Values below \(1\) indicate that non-significant studies are reported less often than significant ones.

The omega vector has one panel per interval (the reference \([0, 0.025]\) panel shows the spike at \(1\)). `plot_weightfunction()` then visualizes the same weights as a step function over the one-sided p-value scale, with the prior band overlaid.

    par(mfrow = c(1, 2), mar = c(4, 4, 2, 1), cex.axis = 0.8, cex.lab = 0.8, cex.main = 0.75)
    plot(fit_selmodel_brma, parameter = "omega", prior = TRUE, xlim = c(0, 1))

    par(mar = c(4, 4, 2, 1), cex.axis = 0.8, cex.lab = 0.8, cex.main = 0.75)
    plot_weightfunction(fit_selmodel_brma, prior = TRUE, main = "Weight-function")

The bias-corrected `mu` posterior is shifted toward zero relative to a `brma()` random-effects fit, with the magnitude of the shift driven by the gap between the prior and posterior of `omega[0.025, 1]`.

### Asymmetric Sampling Funnels

`funnel()` extends the standard funnel plot by drawing the full sampling distribution implied by the fitted model, including the publication-bias adjustment. With a no-bias `brma()` fit the funnel is symmetric; with a bias-adjustment model the funnel becomes asymmetric in the direction the model identifies as suppressed. Comparing the three default fits side by side shows how each adjustment encodes a different mechanism.

    par(mfrow = c(1, 3), mar = c(4, 4, 2, 1), cex.axis = 0.8, cex.lab = 0.8, cex.main = 0.75)
    xlim <- c(-2, 2.5); ylim <- c(0, 0.8)
    funnel(fit_PET_brma,      main = "PET",       xlim = xlim, ylim = ylim)
    funnel(fit_PEESE_brma,    main = "PEESE",     xlim = xlim, ylim = ylim)
    funnel(fit_selmodel_brma, main = "Sel. Mod.", xlim = xlim, ylim = ylim)

PET tilts the funnel by adding a linear effect of the standard error, so the centerline shifts away from zero in the direction of the dominant effect as precision drops. PEESE produces the same kind of shift but curved (quadratic in standard error), so the displacement grows faster for imprecise studies and the funnel walls bow outward. The selection-model funnel keeps the centerline on the random-effects `mu`, but the implied sampling density on each side of the \(p < 0.025\) contour differs: the non-significant region is down-weighted by `omega[0.025, 1]`, so non-significant studies are predicted to appear less often than significant ones at the same precision.

## Flexible Bias Prior Distributions

The default prior distributions above encode a one-sided publication-selection mechanism: PET and PEESE are constrained to be non-negative, and the weight function is constrained to be monotonically non-increasing in p-value. These restrictions are usually appropriate in practice and help regularize estimates from the bias-adjusted models. Analysts might still be interested in specifying more flexible prior distributions for the bias adjustment. Each of `bPET()`, `bPEESE()`, and `bselmodel()` accepts a `prior_bias` argument that replaces the default with any user-specified prior distribution of the matching type. Below we relax each default in turn and place the restricted and relaxed posteriors side by side. The relaxed prior distributions let the posterior settle wherever the data prefer rather than being pinned against the boundary; conversely, when a relaxed posterior concentrates on the same side as its default counterpart, that agreement is data-driven and not a consequence of the prior constraint.

### PET without truncation

For PET the natural relaxation is to drop the `[0, Inf]` truncation on the bias coefficient. We keep the default Cauchy family and scale (`Cauchy(0, 1)`) so the comparison isolates the effect of removing the lower bound; the prior distribution now puts equal mass on negative-bias regions, where small studies pull the estimate up rather than down.

    fit_PET_brma_flex <- bPET(
      yi = yi, vi = vi, measure = "SMD",
      prior_bias = prior_PET(
        distribution = "cauchy",
        parameters = list(location = 0, scale = 1),
        truncation = list(lower = -Inf, upper = Inf)
      ),
      data = dat, seed = 1
    )

    summary(fit_PET_brma_flex, include_mcmc_diagnostics = FALSE)
    #>
    #> Bayesian Random-Effects PET Model (k = 81)
    #>
    #> Estimates
    #>       Mean    SD  0.025    0.5 0.975
    #> mu  -0.004 0.102 -0.207 -0.004 0.191
    #> tau  0.308 0.040  0.234  0.305 0.391
    #>
    #> Publication Bias
    #>      Mean    SD 0.025   0.5 0.975
    #> PET 0.933 0.408 0.167 0.929 1.755

The two panels below show the prior and posterior of the `PET` coefficient under the default truncated prior distribution (left) and the relaxed full-real-line prior distribution (right). The dashed line at `PET = 0` marks where the default prior distribution is bounded and where the relaxed prior distribution places mass on both sides.

    par(mfrow = c(1, 2), mar = c(4, 4, 2, 1), cex.axis = 0.8, cex.lab = 0.8, cex.main = 0.85)
    plot(fit_PET_brma,      parameter = "PET", prior = TRUE,
      main = "PET (default)",     xlim = c(-2, 4))
    abline(v = 0, lty = 2, col = "grey50")
    plot(fit_PET_brma_flex, parameter = "PET", prior = TRUE,
      main = "PET (untruncated)", xlim = c(-2, 4))
    abline(v = 0, lty = 2, col = "grey50")

### PEESE without truncation

PEESE is relaxed analogously: keep `Cauchy(0, 5)` and remove the lower bound, so the prior distribution is symmetric around zero.

    fit_PEESE_brma_flex <- bPEESE(
      yi = yi, vi = vi, measure = "SMD",
      prior_bias = prior_PEESE(
        distribution = "cauchy",
        parameters = list(location = 0, scale = 5),
        truncation = list(lower = -Inf, upper = Inf)
      ),
      data = dat, seed = 1
    )

    summary(fit_PEESE_brma_flex, include_mcmc_diagnostics = FALSE)
    #>
    #> Bayesian Random-Effects PEESE Model (k = 81)
    #>
    #> Estimates
    #>      Mean    SD  0.025   0.5 0.975
    #> mu  0.093 0.064 -0.034 0.093 0.219
    #> tau 0.305 0.041  0.228 0.303 0.392
    #>
    #> Publication Bias
    #>        Mean    SD 0.025   0.5 0.975
    #> PEESE 1.827 0.780 0.306 1.825 3.351

    par(mfrow = c(1, 2), mar = c(4, 4, 2, 1), cex.axis = 0.8, cex.lab = 0.8, cex.main = 0.85)
    plot(fit_PEESE_brma, parameter = "PEESE", prior = TRUE, main = "PEESE (default)", xlim = c(-5, 10))
    abline(v = 0, lty = 2, col = "grey50")
    plot(fit_PEESE_brma_flex, parameter = "PEESE", prior = TRUE, main = "PEESE (untruncated)", xlim = c(-5, 10))
    abline(v = 0, lty = 2, col = "grey50")

### Selection-model Weight Prior Distributions

The weight on each non-reference p-value bin is built from a weight prior helper exported from the `BayesTools` package. Choosing a different helper changes which weight vectors are a priori possible.

Helper | What the prior distribution allows | Notes
--- | --- | ---
`wf_cumulative(alpha)` | monotone decreasing weights via a Dirichlet on cumulative bin probabilities; non-significant intervals cannot exceed the most-significant interval | the default; matches `metafor::selmodel(..., decreasing = TRUE)` and the canonical 3PSM-style assumption
`wf_fixed(omega)` | the weight vector is fixed to the supplied values; no parameters are sampled | sensitivity analyses with externally specified weights
`wf_independent(prior, scale = "omega")` | independent prior distribution placed directly on each non-reference `omega`; the prior distribution must have non-negative support (e.g., `Beta` or a positive-truncated distribution) | non-monotone weights drawn independently from a custom prior distribution
`wf_independent(prior, scale = "log_omega")` | independent prior distribution placed on `log(omega)`, with `omega = exp(log_omega)` | non-monotone weights drawn independently from a custom prior distribution

The two `wf_independent` scales allow different parameterizations. The `"omega"` scale breaks monotonicity and specifies weights directly on the relative probability scale. The `"log_omega"` scale allows specifying the prior distributions as multiplicative factors against the baseline weight via log scale.

We refit the selection model with a `Normal(0, 0.5)` prior distribution on `log_omega`, which is centered at “no selection” (`omega = 1`) and places roughly 95% of its prior mass on `omega` between \(0.37\) and \(2.7\):

    fit_selmodel_brma_flex <- bselmodel(
      yi = yi, vi = vi, measure = "SMD",
      prior_bias = prior_weightfunction(
        "one-sided",
        steps   = c(0.025),
        weights = wf_independent(
          prior("normal", parameters = list(mean = 0, sd = 0.5)),
          scale = "log_omega"
        )
      ),
      data = dat, seed = 1
    )

    summary(fit_selmodel_brma_flex, include_mcmc_diagnostics = FALSE)
    #>
    #> Bayesian Random-Effects Selection Model (k = 81)
    #>
    #> Estimates
    #>      Mean    SD 0.025   0.5 0.975
    #> mu  0.167 0.060 0.057 0.165 0.291
    #> tau 0.306 0.045 0.223 0.303 0.402
    #>
    #> Publication Bias
    #>                 Mean    SD 0.025   0.5 0.975
    #> omega[0,0.025] 1.000 0.000 1.000 1.000 1.000
    #> omega[0.025,1] 0.767 0.266 0.371 0.726 1.384
    #> P-value intervals for publication bias weights omega correspond to one-sided p-values.

The dashed line at `omega = 1` marks the upper bound of the default Dirichlet weight, where the `log_omega` prior distribution places equal mass on both sides.

    par(mfrow = c(1, 2), mar = c(4, 4, 2, 1), cex.axis = 0.8, cex.lab = 0.8, cex.main = 0.85)
    plot(fit_selmodel_brma, parameter = "omega", prior = TRUE, main = "omega (default)", xlim = c(0, 3))
    abline(v = 1, lty = 2, col = "grey50")

    plot(fit_selmodel_brma_flex, parameter = "omega", prior = TRUE, main = "omega (log_omega)", xlim = c(0, 3))
    abline(v = 1, lty = 2, col = "grey50")

## Other Inference Helpers

`bPET()`, `bPEESE()`, and `bselmodel()` accept the same `mods`, `scale`, and `cluster` arguments as `brma()`, so meta-regression, location-scale, and multilevel bias-adjustment models are directly available. The fits are also `brma` objects, so all summaries, plots, predictions, and diagnostics examined in the Bayesian Meta-Analysis vignette work the same way here.

To average across bias-adjustment models rather than commit to a single one, use `RoBMA()`; see the Robust Bayesian Meta-Analysis vignette for the model-averaged workflow and the available `model_type` presets.

## References

Bartoš, F., & Maier, M. (2020). RoBMA: An R package for robust Bayesian meta-analyses. https://CRAN.R-project.org/package=RoBMA
Bartoš, F., Maier, M., Wagenmakers, E.-J., Doucouliagos, H., & Stanley, T. D. (2023). Robust Bayesian meta-analysis: Model-averaging across complementary publication bias adjustment methods. Research Synthesis Methods, 14(1), 99–116. https://doi.org/10.1002/jrsm.1594
Stanley, T. D., & Doucouliagos, H. (2014). Meta-regression approximations to reduce publication selection bias. Research Synthesis Methods, 5(1), 60–78. https://doi.org/10.1002/jrsm.1095
Vevea, J. L., & Hedges, L. V. (1995). A general linear model for estimating effect size in the presence of publication bias. Psychometrika, 60(3), 419–435. https://doi.org/10.1007/BF02294384
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03