dpmeta: Bayesian Meta-Analysis with Dirichlet Process Priors

In jarbes: Just a Rather Bayesian Evidence Synthesis

Bayesian Meta-Analysis with Dirichlet Process Priors

Description

This function performers a Bayesian meta-analysis with DP as random effects

Usage

dpmeta(
  data,
  mean.mu.0 = 0,
  sd.mu.0 = 10,
  scale.sigma.between = 0.5,
  df.scale.between = 1,
  alpha.0 = 0.03,
  alpha.1 = 10,
  K = 30,
  nr.chains = 2,
  nr.iterations = 10000,
  nr.adapt = 1000,
  nr.burnin = 1000,
  nr.thin = 1,
  parallel = NULL
)

Arguments

data	A data frame with at least two columns with the following names: 1) TE = treatment effect, 2) seTE = the standard error of the treatment effect.
mean.mu.0	Prior mean of the mean of the base distribution default value is mean.mu.0 = 0.
sd.mu.0	Prior standard deviation of the base distribution, the default value is 10.
scale.sigma.between	Prior scale parameter for scale gamma distribution for the precision between studies. The default value is 0.5.
df.scale.between	Degrees of freedom of the scale gamma distribution for the precision between studies. The default value is 1, which results in a Half Cauchy distribution for the standard deviation between studies. Larger values e.g. 30 corresponds to a Half Normal distribution.
alpha.0	Lower bound of the uniform prior for the concentration parameter for the DPM, default value is alpha.0 = 0.03.
alpha.1	Upper bound of the uniform prior for the concentration parameter for the DPM, default value is alpha.1 = 10.
K	Maximum number of clusters in the DP, default value is K = 30.
nr.chains	Number of chains for the MCMC computations, default 2.
nr.iterations	Number of iterations after adapting the MCMC, default is 10000. Some models may need more iterations.
nr.adapt	Number of iterations in the adaptation process, default is 1000. Some models may need more iterations during adptation.
nr.burnin	Number of iteration discard for burn-in period, default is 1000. Some models may need a longer burnin period.
nr.thin	Thinning rate, it must be a positive integer, the default value 1
parallel	NULL -> jags, 'jags.parallel' -> jags.parallel execution

Details

The results of the object of the class bcmeta can be extracted with R2jags or with rjags. In addition a summary, a print and a plot functions are implemented for this type of object.

Value

This function returns an object of the class "dpmeta". This object contains the MCMC output of each parameter and hyper-parameter in the model and the data frame used for fitting the model.

References

Verde, P.E. (2021) A Bias-Corrected Meta-Analysis Model for Combining Studies of Different Types and Quality. Biometrical Journal; 1–17.

Examples

## Not run: 
library(jarbes)


# Example: Stemcells

data("stemcells")
stemcells$TE = stemcells$effect.size
stemcells$seTE = stemcells$se.effect

bm1 = dpmmeta(stemcells)
summary(bm1)
plot(bm1, x.lim = c(-1, 7), y.lim = c(0, 1))

diagnostic(bm1, study.names = stemcells$trial,
           post.p.value.cut = 0.05,
           lwd.forest = 0.5, shape.forest = 4)

diagnostic(bm1, post.p.value.cut = 0.05,
           lwd.forest = 0.5, shape.forest = 4)

## End(Not run)

jarbes documentation built on June 28, 2025, 1:07 a.m.