Package 'publipha'

Title: Bayesian Meta-Analysis with Publications Bias and P-Hacking
Description: Tools for Bayesian estimation of meta-analysis models that account for publications bias or p-hacking. For publication bias, this package implements a variant of the p-value based selection model of Hedges (1992) <doi:10.1214/ss/1177011364> with discrete selection probabilities. It also implements the mixture of truncated normals model for p-hacking described in Moss and De Bin (2019) <arXiv:1911.12445>.
Authors: Jonas Moss [aut, cre] (ORCID: <https://orcid.org/0000-0002-6876-6964>), Trustees of Columbia University [cph]
Maintainer: Jonas Moss <[email protected]>
License: GPL-3
Version: 0.1.2
Built: 2026-06-05 10:49:24 UTC
Source: https://github.com/jonasmoss/publipha

Help Index

The 'publipha' package.

Description

Meta-analysis that corrects for publication selection bias and p-hacking.

References

Hedges, Larry V. "Modeling publication selection effects in meta-analysis." Statistical Science (1992): 246-255.

Moss, Jonas and De Bin, Riccardo. "Modelling publication bias and p-hacking" (2019) arXiv:1911.12445

Stan Development Team (2018). RStan: the R interface to Stan. R package version 2.18.1. https://mc-stan.org

Studies on Effect of Violent Video Games on Negative Outcomes

Description

Results from 477 studies on the effect of violent video games on negative outcomes.

Usage

dat.anderson2010

Format

The data frame contains the following columns:

author character first author
year numeric publication year
outcome character one of seven outcomes
best boolean if TRUE, the was a best practice study
experimental boolean if TRUE, the study was experimental
adult boolean if TRUE, the study subjects were adults
country character country of study
ni numeric sample size
yi numeric observed mean difference in outcome (violent vs. non-violent)
vi numeric corresponding sampling variance

Source

https://github.com/Joe-Hilgard/Anderson-meta

References

Baskerville, N. B., Liddy, C., & Hogg, W. (2012). Systematic review and meta-analysis of practice facilitation within primary care settings. The Annals of Family Medicine, 10(1), 63-74.
Hilgard, J., Engelhardt, C. R., & Rouder, J. N. (2017). Overstated evidence for short-term effects of violent games on affect and behavior: A reanalysis of Anderson et al.(2010).

Studies on Practice Facilitation

Description

Results from 23 studies on the effect of practice facilitation in a primary care setting.

Usage

dat.baskerville2012

Format

The tibble contains the following columns:

author character first author of study
year numeric publication year
design character study design (RCT, C-RCT, or CCT)
blinded boolean if TRUE, the study was blinded
concealed boolean if TRUE, the study was concealed
yi numeric observed mean difference in outcome (facilitated vs non-facilitated)
vi numeric corresponding sampling variance

Source

Baskerville, N. B., Liddy, C., & Hogg, W. (2012). Systematic review and meta-analysis of practice facilitation within primary care settings. The Annals of Family Medicine, 10(1), 63-74.

Studies on the Effect of Power Posing

Description

Results from 27 studies related to power posing.

Usage

dat.cuddy2018

Format

The data frame contains the following columns:

author character first author
year numeric publication year
power boolean if TRUE, the outcome was feeling of power
ease boolean if TRUE, the outcome was an EASE variable
yi numeric standardized mean difference
vi numeric corresponding sampling variance

Details

The data points are taken from the p-curve analysis of Cuddy et al. (2018), restricted to 2 cell designs with mean difference as the outcome variable.

Source

https://osf.io/jx3av/

References

Cuddy, A. J., Schultz, S. J., & Fosse, N. E. (2018). P-curving a more comprehensive body of research on postural feedback reveals clear evidential value for power-posing effects: Reply to Simmons and Simonsohn (2017). Psychological science, 29(4), 656-666.

Meta-analysis on Ego Depletion

Description

Results from 150 studies of ego depletion, the claim that self-control is a limited resource which is tapped whenever self-control is exerted.

Usage

data(dat.dang2018)

Format

The tibble contains the following columns:

author character the last name of the first author and the first letter of the last name of the second author;
year numeric publication year
in_carter character was the study in the meta-analysis of Carter et al. (2015)
study character the number given to the study in the original paper (0 = only one study was reported in the original paper; the addition of a letter indicates subsamples);
dv boolean the dependent variable
iv boolean the independent variable
n1i numeric the number of participants in the depletion condition
n2i numeric the number of participants in the control condition
yi numeric the adjusted standardized mean difference
vi numeric the variance

Source

https://link.springer.com/article/10.1007/s00426-017-0862-x#SupplementaryMaterial)

References

Dang, J. (2018). An updated meta-analysis of the ego depletion effect. Psychological Research, 82(4), 645-651.

Carter, E. C., Kofler, L. M., Forster, D. E., & McCullough, M. E. (2015). A series of meta-analytic tests of the depletion effect: self-control does not seem to rely on a limited resource. Journal of Experimental Psychology: General, 144(4), 796.

Effect Sizes from 875 Studies in Psychology.

Description

Effect sizes from 875 studies in psychology. Adopted from Motyl et al. (2017).

Usage

data(dat.motyl2017)

Format

The tibble contains the following columns:

author character first author of study
year numeric publication year
study numeric the number given to the study in the original paper (0 = only one study was reported in the original paper;
journal character journal where the study was published
concealed character design of the study; "Between", "Within", or "Mixed"
experimental numeric TRUE for an experimental study
ni numeric sample size
yi numeric observed mean difference in outcome
vi numeric corresponding sampling variance

Source

https://osf.io/he8mu/

References

Motyl, M., Demos, A. P., Carsel, T. S., Hanson, B. E., Melton, Z. J., Mueller, A. B., ... & Yantis, C. (2017). The state of social and personality science: Rotten to the core, not so bad, getting better, or getting worse?. Journal of personality and social psychology, 113(1), 34.

Extract Parameters from an mafit Object

Description

Extract samples from a model of class mafit and apply a function fun to them.

Usage

extract_theta0(object, fun = mean)

extract_theta(object, fun = mean, i)

extract_tau(object, fun = mean)

extract_eta(object, fun = mean, i)

extract_isq(object, fun = mean)

Arguments

object

an object of class mafit.
fun

the function to be applied to the fitted parameters.
i

an optional index specifying which parameter to apply fun to. Only for extract_eta and extract_theta.

Details

Support parameters for extraction are: The meta-analytic mean theta0, the individual means theta, the heterogeneity parameter tau, the selection bias parameter eta, and the I squared isq. See Higgins and Thompson (2002) for details about I squared.

All extract_ functions are wrappers around rstan::extract.

Value

The result of FUN being applied to all estimated parameters of object.

References

Higgins, J. P., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta-analysis. Statistics in medicine, 21(11), 1539-1558.

Examples

set.seed(313)
model <- publipha::psma(yi = yi, vi = vi, data = dat.baskerville2012)
extract_theta0(model, mean) # [1] extract_theta0(model, mean)
extract_theta0(model, sd) # [1] 0.1095921
extract_tau(model, mean) # [1] 0.1315312
extract_theta(model, hist, i = 5)

Calculate the loo for an ma object.

Description

Computes PSIS-LOO CV, approximate leave-one-out cross-validation using Pareto smoothed importance sampling, see loo.

Usage

## S4 method for signature 'mafit'
loo(x, ...)

Arguments

x

an object of class mafit.
...

passed to loo. Only

Details

... affect the function through two parameters, marginal and lower_bound. When marginalis TRUE, the PSIS-LOO CV is based on the marginal likelihood, i.e. with the dependence on theta integrated out. marginal defaults to TRUE. lower_bound species the lower bound where log-likelihoods are dropped; this is only used in the p-hacking model and defaults to -6.

Value

A loo object.

Examples

phma_model <- phma(yi, vi, data = metadat::dat.begg1989)
psma_model <- psma(yi, vi, data = metadat::dat.begg1989)
loo(phma_model)
loo(psma_model)

Meta-analysis Correcting for Publication Bias or p-hacking

Description

Bayesian random effects meta-analysis. Correct for publication bias, correct for p-hacking, or run an ordinary meta-analysis without any correction.

Usage

ma(
  yi,
  vi,
  bias = c("publication selection", "p-hacking", "none"),
  data,
  alpha = c(0, 0.025, 0.05, 1),
  prior = NULL,
  tau_prior = c("half-normal", "uniform", "inv_gamma"),
  ...
)

psma(
  yi,
  vi,
  data,
  alpha = c(0, 0.025, 0.05, 1),
  prior = NULL,
  tau_prior = c("half-normal", "uniform", "inv_gamma"),
  ...
)

phma(
  yi,
  vi,
  data,
  alpha = c(0, 0.025, 0.05, 1),
  prior = NULL,
  tau_prior = c("half-normal", "uniform", "inv_gamma"),
  ...
)

cma(
  yi,
  vi,
  data,
  prior = NULL,
  tau_prior = c("half-normal", "uniform", "inv_gamma"),
  ...
)

allma(
  yi,
  vi,
  data,
  alpha = c(0, 0.025, 0.05, 1),
  prior = NULL,
  tau_prior = c("half-normal", "uniform", "inv_gamma"),
  ...
)

Arguments

yi

Numeric vector of length codek with observed effect size estimates.
vi

Numeric vector of length codek with sampling variances.
bias

String; If "publication bias", corrects for publication bias. If "p-hacking", corrects for p-hacking.
data

Optional list or data frame containing yi and vi.
alpha

Numeric vector; Specifies the cutoffs for significance. Should include 0 and 1. Defaults to (0, 0.025, 0.05, 1).
prior

Optional list of prior parameters. See the details.
tau_prior

Which prior to use for tau, the heterogeneity parameter. Defaults to "half-normal"; "uniform" and "inv_gamma are also supported.
...

Passed to rstan::sampling.

Details

ma does a Bayesian meta-analysis with the type of correction used specified by bias. psma is a wrapper for ma with bias = "publication selection", phma is a wrapper with bias = "p-hacking", while cma has bias = "none". The function allma runs all bias options and returns a list.

The bias options are:

  1. ⁠publication selection⁠: The model of publication bias described in Hedges (1992).

  2. p-hacking: The model for p-hacking described in Moss & De Bin (2019).

  3. none: Classical random effects meta-analysis with no correction for selection bias.

The effect size distribution is normal with mean theta0 and standard deviation tau. The prior for theta0 is normal with parameters theta0_mean (default: 0), theta0_sd (default: 1). eta is the vector of K normalized publication probabilities (publication bias model) or K p-hacking probabilities (p-hacking model). The prior of eta is Dirchlet with parameter eta0, which defaults to rep(1, K) for the publication bias model and the p-hacking model. eta0 is the prior for the Dirichlet distribution over the non-normalized etas in the publication bias model, and they are forced to be decreasing.

The standard prior for tau is half-normal with parameters tau_mean (default: 0), tau_sd (default: 1). If the uniform prior is used, the parameter are u_min (default: 0), and u_max with a default of 3. The inverse Gamma has parameters shape (default: 1) and scale default: 1.

To change the prior parameters, pass them to prior in a list.

Value

An S4 object of class mafit when ma, psma, phma or cma is run. A list of mafit objects when allma is run.

References

Hedges, Larry V. "Modeling publication selection effects in meta-analysis." Statistical Science (1992): 246-255.

Moss, Jonas and De Bin, Riccardo. "Modelling publication bias and p-hacking" (2019) arXiv:1911.12445

Examples

phma_model <- phma(yi, vi, data = metadat::dat.begg1989)

prior <- list(
  eta0 = c(3, 2, 1),
  theta0_mean = 0.5,
  theta0_sd = 10,
  tau_mean = 1,
  tau_sd = 1
)

psma_model <- psma(yi, vi, data = metadat::dat.begg1989, prior = prior)


cma_model <- psma(yi, vi, data = metadat::dat.begg1989, prior = prior)


model <- allma(yi, vi, data = metadat::dat.begg1989, prior = prior)

Class mafit: Fitted Meta-analysis Model

Description

Class mafit: Fitted Meta-analysis Model

Marginal Publication Selection Meta-analysis Model

Description

Density, distribution, and random variate generation for the marginalized distribution of the publication selection meta-analysis model

Usage

dmpsnorm(x, theta0, tau, sigma, alpha = c(0, 0.025, 0.05, 1), eta, log = FALSE)

pmpsnorm(
  q,
  theta0,
  tau,
  sigma,
  alpha = c(0, 0.025, 0.05, 1),
  eta,
  lower.tail = TRUE,
  log.p = FALSE
)

rmpsnorm(n, theta0, tau, sigma, alpha = c(0, 0.025, 0.05, 1), eta)

Arguments

x, q

vector of quantiles.
theta0

vector of means.
tau

vector of heterogeneity parameters.
sigma

vector of study standard deviations.
alpha

vector of thresholds for publication bias.
eta

vector of publication probabilities, normalized to sum to 1.
log, log.p

logical; If TRUE, probabilities are given as log(p).
lower.tail

logical; If TRUE (default), the probabilities are P[X≤x]P[X\leq x] otherwise, P[X≥x]P[X\geq x].
n

number of observations. If length(n) > 1, the length is taken to be the number required.

Details

These functions assume a normal underlying effect size distribution and one-sided selection on the effects. For the fixed effects publication bias model see psnorm.

Value

dmpsnorm gives the density, pmpsnorm gives the distribution function, and rmpsnorm generates random deviates.

References

Hedges, Larry V. "Modeling publication selection effects in meta-analysis." Statistical Science (1992): 246-255.

Moss, Jonas and De Bin, Riccardo. "Modelling publication bias and p-hacking" Forthcoming (2019)

Examples

rmpsnorm(100, theta0 = 0, tau = 0.1, sigma = 0.1, eta = c(1, 0.5, 0.1))

p-hacking Meta-analysis Model

Description

Density, distribution, and random variate generation for the p-hacking meta- analysis model.

Usage

dphnorm(x, theta, sigma, alpha = c(0, 0.025, 0.05, 1), eta, log = FALSE)

rphnorm(n, theta, sigma, alpha = c(0, 0.025, 0.05, 1), eta)

pphnorm(
  q,
  theta,
  sigma,
  alpha = c(0, 0.025, 0.05, 1),
  eta,
  lower.tail = TRUE,
  log.p = FALSE
)

Arguments

x, q

vector of quantiles.
theta

vector of means.
sigma

vector of study standard deviations.
alpha

vector of thresholds for p-hacking.
eta

vector of p-hacking probabilities, normalized to sum to 1.
log, log.p

logical; If TRUE, probabilities are given as log(p).
n

number of observations. If length(n) > 1, the length is taken to be the number required.
lower.tail

logical; If TRUE (default), the probabilities are P[X≤x]P[X\leq x] otherwise, P[X≥x]P[X\geq x].

Details

These functions assume one-sided selection on the effects. alpha contains the selection thresholds and eta the vector of p-hacking probabilities. theta is the true effect, while sigma is the true standard deviation before selection.

Value

dphnorm gives the density, pphnorm gives the distribution function, and rphnorm generates random deviates.

References

Moss, Jonas and De Bin, Riccardo. "Modelling publication bias and p-hacking" Forthcoming (2019)

Examples

rphnorm(100, theta = 0, sigma = 0.1, eta = c(1, 0.5, 0.1))

Publication Selection Meta-analysis Model

Description

Density, distribution, quantile, random variate generation, and expectation calculation for the distribution for the publication selection meta-analysis model

Usage

dpsnorm(x, theta, sigma, alpha = c(0, 0.025, 0.05, 1), eta, log = FALSE)

ppsnorm(
  q,
  theta,
  sigma,
  alpha = c(0, 0.025, 0.05, 1),
  eta,
  lower.tail = TRUE,
  log.p = FALSE
)

rpsnorm(n, theta, sigma, alpha = c(0, 0.025, 0.05, 1), eta)

Arguments

x, q

vector of quantiles.
theta

vector of means.
sigma

vector of study standard deviations.
alpha

vector of thresholds for publication bias.
eta

vector of publication probabilities, normalized to sum to 1.
log, log.p

logical; If TRUE, probabilities are given as log(p).
lower.tail

logical; If TRUE (default), the probabilities are P[X≤x]P[X\leq x] otherwise, P[X≥x]P[X\geq x].
n

number of observations. If length(n) > 1, the length is taken to be the number required.

Details

The effect size distribution for the publication selection model is not normal, but has itself been selected for. These functions assume one-sided selection on the effects. These functions do not assume the existence of an underlying effect size distribution. For these, see mpsnorm.

Value

dpsnorm gives the density, ppsnorm gives the distribution function, and rpsnorm generates random deviates.

References

Hedges, Larry V. "Modeling publication selection effects in meta-analysis." Statistical Science (1992): 246-255.

Moss, Jonas and De Bin, Riccardo. "Modelling publication bias and p-hacking" Forthcoming (2019)

Examples

rpsnorm(100, theta = 0, sigma = 0.1, eta = c(1, 0.5, 0.1))

Selected Normal Effect Size Distribution

Description

Density, random variate generation, and expectation calculation for the effect size distribution of the one-sided normal publication bias model.

Usage

dsnorm(x, theta0, tau, sigma, alpha = c(0, 0.025, 0.05, 1), eta, log = FALSE)

rsnorm(n, theta0, tau, sigma, alpha = c(0, 0.025, 0.05, 1), eta)

esnorm(theta0, tau, sigma, alpha, eta)

Arguments

x

vector of quantiles.
theta0

vector of means.
tau

vector of heterogeneity parameters.
sigma

vector of study standard deviations.
alpha

vector of thresholds for publication bias.
eta

vector of publication probabilities, normalized to sum to 1.
log

logical; If TRUE, probabilities are given as log(p).
n

number of observations. If length(n) > 1, the length is taken to be the number required.

Details

The effect size distribution for the publication selection model is not normal, but has itself been selected for. These functions assume a normal underlying effect size distribution and one-sided selection on the effects.

Value

dsnorm gives the density, psnorm gives the distribution function, and rsnorm generates random deviates.

References

Hedges, Larry V. "Modeling publication selection effects in meta-analysis." Statistical Science (1992): 246-255.

Moss, Jonas and De Bin, Riccardo. "Modelling publication bias and p-hacking" (2019) arXiv:1911.12445

Examples

rsnorm(100, theta0 = 0, tau = 0.1, sigma = 0.1, eta = c(1, 0.5, 0.1))