publipha (version 0.1.2)

ma: 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"),
  ...
)

Value

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

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.

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

# \donttest{
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
)
# \donttest{
psma_model <- psma(yi, vi, data = metadat::dat.begg1989, prior = prior)
# }
# \donttest{
cma_model <- psma(yi, vi, data = metadat::dat.begg1989, prior = prior)
# }
# \donttest{
model <- allma(yi, vi, data = metadat::dat.begg1989, prior = prior)
# }