| Title: | Bayesian Meta-Analysis with Publications Bias and P-Hacking |
| Version: | 0.1.2 |
| 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) <doi:10.48550/arXiv.1911.12445>. |
| License: | GPL-3 |
| Depends: | methods, R (≥ 3.6.0), Rcpp (≥ 0.12.19) |
| Imports: | rstan (≥ 2.18.1), rstantools (≥ 1.5.1), loo, truncnorm |
| LinkingTo: | BH (≥ 1.72.0-2), Rcpp (≥ 0.12.19), RcppEigen (≥ 0.3.3.4.0), rstan (≥ 2.21.8), StanHeaders (≥ 2.21.0-7), RcppParallel |
| Encoding: | UTF-8 |
| LazyData: | true |
| NeedsCompilation: | yes |
| SystemRequirements: | GNU make |
| RoxygenNote: | 7.2.3 |
| Suggests: | testthat (≥ 2.1.0), covr, knitr, rmarkdown, metafor, spelling, metadat |
| Collate: | 'data-anderson2010.R' 'data-baskerville2012.R' 'data-cuddy2018.R' 'data-dang2018.R' 'data-motyl2017.R' 'densities-helpers.R' 'densities-mpsnorm.R' 'densities-phnorm.R' 'densities-psnorm.R' 'densities-snorm.R' 'ma.R' 'generics.R' 'publipha-package.R' 'stanmodels.R' 'tools.R' 'utility.R' |
| Language: | en-US |
| Packaged: | 2023-04-04 12:11:24 UTC; jonas |
| Author: | Jonas Moss  [aut,
cre],
Trustees of Columbia University [cph] |
| Maintainer: | Jonas Moss <jonas.gjertsen@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2023-04-04 16:10:06 UTC |
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
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 |
fun |
the function to be applied to the fitted parameters. |
i |
an optional index specifying which parameter to apply |
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)
Adds named elements to a list when they are not there already.
Description
Adds named elements to a list when they are not there already.
Usage
add_elements(input, ..., .eager = TRUE)
Arguments
input |
List. The input list to manipulate. |
... |
Key value pairs to add to the list provided the key is not already used. |
.eager |
Logical; Should the |
Value
A modified list.
Make lazy list from arguments.
Description
Works with passed ... parameters.
Usage
alist2(...)
Arguments
... |
Parameters to put into the list. |
Value
A lazy list.
Get arguments of the calling function.
Description
Get arguments of the calling function.
Usage
arguments(expand_dots = FALSE)
Arguments
expand_dots |
Logical; If |
Value
The unevaluated argument list given to the calling function.
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
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
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.
Variant of do call with that preserves argument names.
Description
Variant of do call with that preserves argument names.
Usage
do_call(.fn, .args = NULL, ..., .env = parent.frame())
Arguments
.fn |
Function to call. |
.args |
List of arguments to |
... |
Further arguments to |
.env |
The environment where the call is to be evaluated. |
Value
The effect of calling .fn with the supplied arguments in the
specified environment.
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 |
... |
passed to |
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 |
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 |
... |
Passed to |
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:
-
publication selection: The model of publication bias described in Hedges (1992). -
p-hacking: The model for p-hacking described in Moss & De Bin (2019). -
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 |
lower.tail |
logical; If |
n |
number of observations. If |
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))
Normalizing Constants for the Publication Selection Meta-Analysis Model
Description
Normalizing constants for the publication selection meta-Analysis model. These are used in several other functions. The underlying effect size distribution is normal and the selection is one-sided.
Usage
I(sigma, theta, alpha, eta)
J(sigma, theta0, tau, alpha, eta)
Arguments
sigma |
Numeric; The standard deviation of the study, due to sampling error. |
theta |
Numeric; The mean of the underlying effect size. |
alpha |
Numeric vector; Specifies the thresholds for publication bias. |
eta |
Numeric vector; Containing the probabilities of being a study with the given p-value from being published. This is normalized so that the maximal element is 1. |
theta0 |
Numeric; The mean of the underlying effect size distribution. |
tau |
Numeric; The standard deviation of the underlying effect size distribution. |
Details
The function I calculates the normalizing constant for the density of
the observed effect sizes. The function J calculates the
normalizing constant for the density of the effect size distribution.
Value
The normalizing constant.
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 |
n |
number of observations. If |
lower.tail |
logical; If |
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 |
lower.tail |
logical; If |
n |
number of observations. If |
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 |
n |
number of observations. If |
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))