generateBayesianHMAsettings: Generate settings for the Bayesian random-effects...

In EvidenceSynthesis: Synthesizing Causal Evidence in a Distributed Research Network

Generate settings for the Bayesian random-effects hierarchical meta-analysis model

Description

This function generates a settings list for fitting a Bayesian hierarchical meta-analysis model. See computeHierarchicalMetaAnalysis() for more details.

Usage

generateBayesianHMAsettings(
  primaryEffectPriorStd = 1,
  secondaryEffectPriorStd = 1,
  globalExposureEffectPriorMean = c(0),
  globalExposureEffectPriorStd = c(2),
  primaryEffectPrecisionPrior = c(1, 1),
  secondaryEffectPrecisionPrior = c(1, 1),
  errorPrecisionPrior = c(1, 1),
  errorPrecisionStartValue = 1,
  includeSourceEffect = TRUE,
  includeExposureEffect = TRUE,
  exposureEffectCount = 1,
  separateExposurePrior = FALSE,
  chainLength = 1100000,
  burnIn = 1e+05,
  subSampleFrequency = 100
)

Arguments

primaryEffectPriorStd	Standard deviation for the average outcome effect.
secondaryEffectPriorStd	Standard deviation for the average data-source effect.
globalExposureEffectPriorMean	Prior mean for the global main exposure effect; can be a multiple entry vector if there are multiple outcomes of interest
globalExposureEffectPriorStd	Prior standard deviation for the global main exposure effect; can be a multiple entry vector if there are multiple outcomes of interest
primaryEffectPrecisionPrior	Shape and scale for the gamma prior of the precision term in the random effects model (normal) for individual outcome effects.
secondaryEffectPrecisionPrior	Shape and scale for the gamma prior of the precision term in the random effects model (normal) for individual data-source effects.
errorPrecisionPrior	Shape and scale for the gamma prior of the precision term in the normal model for random errors.
errorPrecisionStartValue	Initial value for the error distribution's precision term.
includeSourceEffect	Whether or not to consider the data-source-specific (secondary) random effects. Default is TRUE.
includeExposureEffect	Whether or not to estimate the main effect of interest. Default is TRUE.
exposureEffectCount	Number of main outcomes of interest to estimate effect for? Default = 1
separateExposurePrior	Use a separable prior on the main exposure effect? Default is FALSE.
chainLength	Number of MCMC iterations.
burnIn	Number of MCMC iterations to consider as burn in.
subSampleFrequency	Subsample ("thinning") frequency for the MCMC.

Value

A list with all the settings to use in the computeHierarchicalMetaAnalysis() function.

See Also

computeHierarchicalMetaAnalysis

EvidenceSynthesis documentation built on Aug. 26, 2025, 9:08 a.m.