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  "Title": "Synthesizing Causal Evidence in a Distributed Research Network",
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  "Description": "Routines for combining causal effect estimates and study\ndiagnostics across multiple data sites in a distributed study,\nwithout sharing patient-level data. Allows for normal and\nnon-normal approximations of the data-site likelihood of the\neffect parameter.",
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    "constructDataModel",
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    "createSimulationSettings",
    "customFunction",
    "detectApproximationType",
    "extractSourceSpecificEffects",
    "fitBiasDistribution",
    "generateBayesianHMAsettings",
    "hermiteInterpolation",
    "loadCyclopsLibraryForJava",
    "plotBiasCorrectionInference",
    "plotBiasDistribution",
    "plotCovariateBalances",
    "plotEmpiricalNulls",
    "plotLikelihoodFit",
    "plotMcmcTrace",
    "plotMetaAnalysisForest",
    "plotPerDbMcmcTrace",
    "plotPerDbPosterior",
    "plotPosterior",
    "plotPreparedPs",
    "preparePsPlot",
    "prepareSccsIntervalData",
    "sequentialFitBiasDistribution",
    "simulateMetaAnalysisWithNegativeControls",
    "simulatePopulations",
    "skewNormal",
    "summarizeChain",
    "supportsJava8"
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    {
      "name": "hmaLikelihoodList",
      "title": "Example profile likelihoods for hierarchical meta analysis with bias correction",
      "object": "hmaLikelihoodList",
      "class": [
        "list"
      ],
      "fields": [],
      "table": true,
      "tojson": true
    },
    {
      "name": "likelihoodProfileLists",
      "title": "A bigger example of profile likelihoods for hierarchical meta analysis with bias correction",
      "object": "likelihoodProfileLists",
      "class": [
        "list"
      ],
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      "table": false,
      "tojson": true
    },
    {
      "name": "ncLikelihoods",
      "title": "Example profile likelihoods for negative control outcomes",
      "object": "ncLikelihoods",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "ooiLikelihoods",
      "title": "Example profile likelihoods for a synthetic outcome of interest",
      "object": "ooiLikelihoods",
      "class": [
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      ],
      "fields": [],
      "table": false,
      "tojson": true
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  "_help": [
    {
      "page": "approximateHierarchicalNormalPosterior",
      "title": "Approximate Bayesian posterior for hierarchical Normal model",
      "topics": [
        "approximateHierarchicalNormalPosterior"
      ]
    },
    {
      "page": "approximateLikelihood",
      "title": "Approximate a likelihood function",
      "topics": [
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      ]
    },
    {
      "page": "approximateSimplePosterior",
      "title": "Approximate simple Bayesian posterior",
      "topics": [
        "approximateSimplePosterior"
      ]
    },
    {
      "page": "biasCorrectionInference",
      "title": "Bias Correction with Inference",
      "topics": [
        "biasCorrectionInference"
      ]
    },
    {
      "page": "buildLabelReferences",
      "title": "Build a list of references that map likelihood names to integer labels for later use",
      "topics": [
        "buildLabelReferences"
      ]
    },
    {
      "page": "computeBayesianMetaAnalysis",
      "title": "Compute a Bayesian random-effects meta-analysis",
      "topics": [
        "computeBayesianMetaAnalysis"
      ]
    },
    {
      "page": "computeConfidenceInterval",
      "title": "Compute the point estimate and confidence interval given a likelihood function approximation",
      "topics": [
        "computeConfidenceInterval"
      ]
    },
    {
      "page": "computeFixedEffectMetaAnalysis",
      "title": "Compute a fixed-effect meta-analysis",
      "topics": [
        "computeFixedEffectMetaAnalysis"
      ]
    },
    {
      "page": "computeHierarchicalMetaAnalysis",
      "title": "Compute a Bayesian random-effects hierarchical meta-analysis",
      "topics": [
        "computeHierarchicalMetaAnalysis"
      ]
    },
    {
      "page": "constructDataModel",
      "title": "Construct 'DataModel' objects from approximate likelihood or profile likelihood data",
      "topics": [
        "constructDataModel"
      ]
    },
    {
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      "topics": [
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    {
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      "topics": [
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      "page": "createSimulationSettings",
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      "topics": [
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    },
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      "topics": [
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    {
      "page": "detectApproximationType",
      "title": "Detect the type of likelihood approximation based on the data format",
      "topics": [
        "detectApproximationType"
      ]
    },
    {
      "page": "extractSourceSpecificEffects",
      "title": "Compute source-specific biases and bias-corrected estimates from hierarchical meta analysis results",
      "topics": [
        "extractSourceSpecificEffects"
      ]
    },
    {
      "page": "fitBiasDistribution",
      "title": "Fit Bias Distribution",
      "topics": [
        "fitBiasDistribution"
      ]
    },
    {
      "page": "generateBayesianHMAsettings",
      "title": "Generate settings for the Bayesian random-effects hierarchical meta-analysis model",
      "topics": [
        "generateBayesianHMAsettings"
      ]
    },
    {
      "page": "hermiteInterpolation",
      "title": "Cubic Hermite interpolation using both values and gradients to approximate a log likelihood function",
      "topics": [
        "hermiteInterpolation"
      ]
    },
    {
      "page": "hmaLikelihoodList",
      "title": "Example profile likelihoods for hierarchical meta analysis with bias correction",
      "topics": [
        "hmaLikelihoodList"
      ]
    },
    {
      "page": "likelihoodProfileLists",
      "title": "A bigger example of profile likelihoods for hierarchical meta analysis with bias correction",
      "topics": [
        "likelihoodProfileLists"
      ]
    },
    {
      "page": "loadCyclopsLibraryForJava",
      "title": "Load the Cyclops dynamic C++ library for use in Java",
      "topics": [
        "loadCyclopsLibraryForJava"
      ]
    },
    {
      "page": "ncLikelihoods",
      "title": "Example profile likelihoods for negative control outcomes",
      "topics": [
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      ]
    },
    {
      "page": "ooiLikelihoods",
      "title": "Example profile likelihoods for a synthetic outcome of interest",
      "topics": [
        "ooiLikelihoods"
      ]
    },
    {
      "page": "plotBiasCorrectionInference",
      "title": "Plot bias correction inference",
      "topics": [
        "plotBiasCorrectionInference"
      ]
    },
    {
      "page": "plotBiasDistribution",
      "title": "Plot bias distributions",
      "topics": [
        "plotBiasDistribution"
      ]
    },
    {
      "page": "plotCovariateBalances",
      "title": "Plot covariate balances",
      "topics": [
        "plotCovariateBalances"
      ]
    },
    {
      "page": "plotEmpiricalNulls",
      "title": "Plot empirical null distributions",
      "topics": [
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    },
    {
      "page": "plotLikelihoodFit",
      "title": "Plot the likelihood approximation",
      "topics": [
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    {
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      "title": "Plot MCMC trace",
      "topics": [
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    },
    {
      "page": "plotMetaAnalysisForest",
      "title": "Create a forest plot",
      "topics": [
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      "page": "plotPerDbMcmcTrace",
      "title": "Plot MCMC trace for individual databases",
      "topics": [
        "plotPerDbMcmcTrace"
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    },
    {
      "page": "plotPerDbPosterior",
      "title": "Plot posterior density per database",
      "topics": [
        "plotPerDbPosterior"
      ]
    },
    {
      "page": "plotPosterior",
      "title": "Plot posterior density",
      "topics": [
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    {
      "page": "plotPreparedPs",
      "title": "Plot the propensity score distribution",
      "topics": [
        "plotPreparedPs"
      ]
    },
    {
      "page": "preparePsPlot",
      "title": "Prepare to plot the propensity score distribution",
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    },
    {
      "page": "prepareSccsIntervalData",
      "title": "Prepare SCCS interval data for pooled analysis",
      "topics": [
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    },
    {
      "page": "sequentialFitBiasDistribution",
      "title": "Fit Bias Distribution Sequentially or in Groups",
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    },
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      "page": "simulateMetaAnalysisWithNegativeControls",
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      "topics": [
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      "page": "simulatePopulations",
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      "page": "skewNormal",
      "title": "The skew normal function to approximate a log likelihood function",
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      "title": "Utility function to summarize MCMC samples (posterior mean, median, HDI, std, etc.)",
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        "summarizeChain"
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      "page": "supportsJava8",
      "title": "Determine if Java virtual machine supports Java",
      "topics": [
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      ]
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      "source": "BayesianBiasCorrection.Rmd",
      "filename": "BayesianBiasCorrection.html",
      "title": "Bayesian adaptive bias correction using profile likelihoods",
      "author": "Fan Bu",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Learning bias distributions from negative control outcomes",
        "Perform Bayesian adaptive bias correction"
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      "created": "2023-04-22 06:36:43",
      "modified": "2025-08-26 05:57:16",
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      "source": "VideoVignette.Rmd",
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        "Simulate data",
        "Fit a model locally",
        "Approximate the likelihood function at one site",
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        "Synthesize evidence",
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