Bayesian Network Meta-Analysis — bayesiannetworkma • ClinicoPath

Advanced Bayesian network meta-analysis for multiple treatment comparisons using network geometry. Supports direct and indirect comparisons, network coherence assessment, and ranking of multiple interventions with uncertainty quantification for clinical decision making.

Usage

bayesiannetworkma(
  data,
  study_id,
  treatment,
  outcome,
  sample_size,
  outcomeLevel,
  standard_error = NULL,
  control_rate = NULL,
  network_type = "mixed_evidence",
  outcome_type = "binary_or",
  reference_treatment,
  baseline_model = "exchangeable",
  random_effects_model = "random_univariate",
  heterogeneity_model = "common",
  correlation_structure = "unstructured",
  treatment_effect_prior = "vague_normal",
  heterogeneity_prior = "half_normal",
  prior_mean_effect = 0,
  prior_sd_effect = 10,
  prior_heterogeneity_sd = 1,
  assess_coherence = TRUE,
  coherence_method = "node_splitting",
  inconsistency_threshold = 0.05,
  mcmc_samples = 10000,
  mcmc_burnin = 5000,
  mcmc_thin = 2,
  mcmc_chains = 3,
  ranking_analysis = TRUE,
  ranking_measure = "sucra",
  pairwise_probabilities = TRUE,
  superiority_threshold = 0,
  model_selection = TRUE,
  model_comparison_criteria = "dic",
  network_plot = TRUE,
  network_layout = "spring",
  edge_weights = "n_studies",
  show_network_summary = TRUE,
  show_treatment_effects = TRUE,
  show_heterogeneity_analysis = TRUE,
  show_coherence_results = TRUE,
  show_ranking_results = TRUE,
  show_forest_plots = TRUE,
  show_league_table = TRUE,
  show_convergence_diagnostics = TRUE,
  show_interpretation = TRUE,
  meta_regression_covariates = NULL,
  treatment_classes = NULL,
  multi_arm_correction = TRUE,
  zero_events_correction = 0.5,
  clinical_context = "therapeutic",
  confidence_level = 0.95,
  set_seed = TRUE,
  seed_value = 42,
  parallel_processing = TRUE,
  n_cores = 2
)

Arguments

data: The data as a data frame for Bayesian network meta-analysis.
study_id: .
treatment: .
outcome: .
sample_size: .
outcomeLevel: .
standard_error: For continuous outcomes
control_rate: Baseline event rate for binary outcomes
network_type: .
outcome_type: .
reference_treatment: Reference treatment for network comparisons
baseline_model: .
random_effects_model: .
heterogeneity_model: .
correlation_structure: .
treatment_effect_prior: .
heterogeneity_prior: .
prior_mean_effect: .
prior_sd_effect: .
prior_heterogeneity_sd: .
assess_coherence: .
coherence_method: .
inconsistency_threshold: .
mcmc_samples: .
mcmc_burnin: .
mcmc_thin: .
mcmc_chains: .
ranking_analysis: .
ranking_measure: .
pairwise_probabilities: .
superiority_threshold: Threshold for clinically meaningful superiority
model_selection: .
model_comparison_criteria: .
network_plot: .
network_layout: .
edge_weights: .
show_network_summary: .
show_treatment_effects: .
show_heterogeneity_analysis: .
show_coherence_results: .
show_ranking_results: .
show_forest_plots: .
show_league_table: .
show_convergence_diagnostics: .
show_interpretation: .
meta_regression_covariates: .
treatment_classes: For class-effect models
multi_arm_correction: .
zero_events_correction: .
clinical_context: .
confidence_level: .
set_seed: .
seed_value: .
parallel_processing: .
n_cores: .

Value

A results object containing:

`results$networkSummary`					a table
`results$networkCharacteristics`					a table
`results$treatmentEffects`					a table
`results$heterogeneityAnalysis`					a table
`results$coherenceAssessment`					a table
`results$treatmentRankings`					a table
`results$pairwiseComparisons`					a table
`results$leagueTable`					a table
`results$modelComparison`					a table
`results$convergenceDiagnostics`					a table
`results$metaRegressionResults`					a table
`results$sensitivityAnalysis`					a table
`results$clinicalInterpretation`					a html
`results$methodsExplanation`					a html
`results$networkPlot`					an image
`results$forestPlots`					an image
`results$rankingPlots`					an image
`results$heterogeneityPlots`					an image
`results$coherencePlots`					an image
`results$posteriorDistributionPlots`					an image
`results$convergencePlots`					an image
`results$comparisonMatrix`					an image

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$networkSummary$asDF

as.data.frame(results$networkSummary)

Examples

# Bayesian network meta-analysis
bayesiannetworkma(
    data = network_data,
    study_id = "study",
    treatment = "treatment",
    outcome = "response",
    sample_size = "n",
    network_type = "mixed_evidence"
)