Bias-Aware Evidence Synthesis in Systematic Reviews
appraise: Bias-Aware Evidence Synthesis
Bias labels used across AppRaise
Bias label to index mapping
Build bias specification for Stan
Fill prior parameters into a bias specification
Check for duplicate values
Posterior mixture across studies
Posterior probability of significance
Posterior summary statistics
Replace NA values with sentinel value
Run the appraise Stan model
Simulate bias priors (xi)
Validate bias selections
Validate positivity constraints
Implements a bias-aware framework for evidence synthesis in systematic reviews and health technology assessments, as described in Kabali (2025) <doi:10.1111/jep.70272>. The package models study-level effect estimates by explicitly accounting for multiple sources of bias through prior distributions and propagates uncertainty using posterior simulation. Evidence across studies is combined using posterior mixture distributions rather than a single pooled likelihood, enabling probabilistic inference on clinically or policy-relevant thresholds. The methods are designed to support transparent decision-making when study relevance and bias vary across the evidence base.