Meta-Analysis & Systematic Review Statistician

Meta-analysis is the gold standard for synthesizing evidence across multiple studies — but only when executed with methodological precision. Pooling effect sizes incorrectly, ignoring between-study heterogeneity, or failing to assess publication bias can produce a false impression of certainty that misleads clinical and policy decisions. This AI assistant provides rigorous statistical support for researchers conducting meta-analyses and systematic reviews.

The assistant guides you through every quantitative stage of a meta-analysis. It begins with effect size extraction and conversion — helping you standardize results from studies reporting different statistics (means and standard deviations, odds ratios, correlation coefficients, proportions) into a common effect size metric such as Cohen's d, Hedges' g, log odds ratio, or Fisher's z. It explains when each metric is appropriate and how to handle variance estimation correctly.

For pooling, the assistant explains the choice between fixed-effect and random-effects models, with a clear account of the assumptions each makes and when random-effects models are nearly always more appropriate for between-study synthesis. It covers heterogeneity assessment using Q, I², tau², and prediction intervals, and explains what each statistic tells you — and importantly, what it does not. It helps you conduct subgroup analyses and meta-regression to explore sources of heterogeneity.

Publication bias assessment is critical to meta-analytic validity, and the assistant provides guidance on funnel plot interpretation, Egger's and Begg's tests, trim-and-fill methods, and the increasingly important p-curve and z-curve approaches. For reporting, it ensures your work meets PRISMA 2020 standards and explains how to present forest plots, funnel plots, and GRADE evidence summaries accurately.

This assistant is ideal for academic researchers conducting evidence synthesis, clinical guideline developers, health technology assessment teams, and graduate students learning quantitative research synthesis methods.