Compute a Bayesian random-effects meta-analysis

Compute a Bayesian meta-analysis using the Markov chain Monte Carlo (MCMC) engine BEAST. A normal and half-normal prior are used for the mu and tau parameters, respectively, with standard deviations as defined by the priorSd argument.

computeBayesianMetaAnalysis(
  data,
  chainLength = 1100000,
  burnIn = 1e+05,
  subSampleFrequency = 100,
  priorSd = c(2, 0.5),
  alpha = 0.05,
  robust = FALSE,
  df = 4,
  seed = 1,
  showProgressBar = TRUE
)

Arguments

data: A data frame containing either normal, skew-normal, custom parametric, or grid likelihood data, with one row per database.
chainLength: Number of MCMC iterations.
burnIn: Number of MCMC iterations to consider as burn in.
subSampleFrequency: Subsample frequency for the MCMC.
priorSd: A two-dimensional vector with the standard deviation of the prior for mu and tau, respectively.
alpha: The alpha (expected type I error) used for the credible intervals.
robust: Whether or not to use a t-distribution model; default: FALSE.
df: Degrees of freedom for the t-model, only used if robust is TRUE.
seed: The seed for the random number generator.
showProgressBar: Showing a progress bar for MCMC?

Value

A data frame with the point estimates and 95% credible intervals for the mu and tau parameters (the mean and standard deviation of the distribution from which the per-site effect sizes are drawn). Attributes of the data frame contain the MCMC trace and the detected approximation type.

Examples

# Simulate some data for this example:
populations <- simulatePopulations()

# Fit a Cox regression at each data site, and approximate likelihood function:
fitModelInDatabase <- function(population) {
  cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
    data = population,
    modelType = "cox"
  )
  cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
  approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")
  return(approximation)
}
approximations <- lapply(populations, fitModelInDatabase)
approximations <- do.call("rbind", approximations)

# At study coordinating center, perform meta-analysis using per-site approximations:
estimate <- computeBayesianMetaAnalysis(approximations)
#> Detected data following custom parameric distribution
#> Detected data following custom parameric distribution
#> Performing MCMC. This may take a while
estimate
#>          mu     mu95Lb  mu95Ub      muSe       tau    tau95Lb  tau95Ub
#> 1 0.4552508 -0.3850542 1.27779 0.4234876 0.4395055 0.01287681 1.015193
#>       logRr   seLogRr
#> 1 0.4552508 0.4234876

# (Estimates in this example will vary due to the random simulation)

Arguments

Value

See also

Examples