Speaker
Description
Conventional two-stage procedures for binary-outcome meta-analysis use fixed plug-in estimates of within-study variances and depend heavily on large-sample normal approximations. These assumptions are often untenable and can lead to inaccurate inference, especially in sparse settings. Likelihood-based random-effects models, including the binomial–normal and the hypergeometric–normal (HGN) formulations, address several of these limitations but remain confined to the odds-ratio metric, which hampers clinical interpretability and prevents direct estimation of the risk ratio. To address this gap, we introduce a likelihood framework that extends the HGN model to permit direct inference on the risk ratio. The key innovation is a pseudo-observation augmentation strategy that maintains the conditional-likelihood properties of the HGN model while producing an unbiased estimating equation for the log–risk ratio. We further enhance finite-sample performance through jackknife-based adjustments for bias and variance. Real-world examples illustrate that the method provides transparent, interpretable, and computationally efficient inference for risk-ratio meta-analysis.
21429418888