P-Uniform*

When to use it

Use it when you have an effects table and need a defensible pooled estimate.

Takes meta-analysis out of a calculator output and binds it to a decision trail: effect size, heterogeneity, model selection and provenance.

Limitations and scope

• Results are conditional on the studies, corpus and parameters you supply, and on the chosen model. The analysis summarises the evidence as reported; it does not establish causation.
• Every figure carries its source, and every run is reproducible bit-for-bit from the same inputs, parameters and engine version.

In this family

• Univariate Meta-AnalysisPool one effect size per study into a single summary estimate with heterogeneity diagnostics.
• Multilevel Meta-AnalysisTwo- or three-level models for dependent effect sizes nested within studies or labs.
• GLMM Meta-AnalysisGeneralised linear mixed models for binary/count outcomes without normal approximation.
• Network Meta-AnalysisMixed treatment comparison across ≥3 interventions with a connected evidence network.
• Diagnostic Test AccuracyBivariate sensitivity/specificity model with a summary ROC curve.
• Dose–Response Meta-AnalysisModel the shape of an exposure–outcome relationship across dose levels.
• P-Curve AnalysisTest the significant p-values for evidential value vs p-hacking via the p-curve's right-skew (Simonsohn et al. 2014).
• Trial Sequential AnalysisAdjust cumulative meta-analysis for repeated significance testing — is the evidence conclusive, or is more information needed (Wetterslev et al. 2008)?
• MA Power AnalysisProspective / retrospective power for a meta-analytic design specification.