EvidenceSynthesis (version 1.1.0)

plotMetaAnalysisForest: Create a forest plot

Description

Creates a forest plot of effect size estimates, including the summary estimate.

Usage

plotMetaAnalysisForest(
  data,
  labels,
  estimate,
  xLabel = "Relative risk",
  summaryLabel = "Summary",
  limits = c(0.1, 10),
  alpha = 0.05,
  showPredictionInterval = TRUE,
  showLikelihood = TRUE,
  fileName = NULL
)

Value

A Ggplot object. Use the ggplot2::ggsave function to save to file.

Arguments

data

A data frame containing either normal, skew-normal, custom parametric, or grid likelihood data. One row per database.

labels

A vector of labels for the data sources.

estimate

The meta-analytic estimate as created using either computeFixedEffectMetaAnalysis or computeBayesianMetaAnalysis function.

xLabel

The label on the x-axis: the name of the effect estimate.

summaryLabel

The label for the meta-analytic estimate.

limits

The limits of the effect size axis.

alpha

The alpha (expected type I error).

showPredictionInterval

Show the prediction interval (for random effects models).

showLikelihood

Show the likelihood curve for each estimate?

fileName

Name of the file where the plot should be saved, for example 'plot.png'. See the function ggplot2::ggsave ifor supported file formats.

Details

Creates a forest plot of effect size estimates, including a meta-analysis estimate.

Examples

# Simulate some data for this example:
populations <- simulatePopulations()
labels <- paste("Data site", LETTERS[1:length(populations)])

# 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 = "grid with gradients")
  return(approximation)
}
approximations <- lapply(populations, fitModelInDatabase)

# At study coordinating center, perform meta-analysis using per-site approximations:
estimate <- computeBayesianMetaAnalysis(approximations)
plotMetaAnalysisForest(approximations, labels, estimate)

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