Li Lu, Lin Lifeng, Cappelleri Joseph C, Chu Haitao, Chen Yong
Center for Health Analytics and Synthesis of Evidence, the Perelman School of Medicine, University of Pennsylvania, PA, USA.
Applied Mathematics and Computational Science, University of Pennsylvania, PA, USA.
medRxiv. 2024 Jul 25:2024.07.25.24310959. doi: 10.1101/2024.07.25.24310959.
Double-zero-event studies (DZS) pose a challenge for accurately estimating the overall treatment effect in meta-analysis. Current approaches, such as continuity correction or omission of DZS, are commonly employed, yet these ad hoc methods can yield biased conclusions. Although the standard bivariate generalized linear mixed model can accommodate DZS, it fails to address the potential systemic differences between DZS and other studies. In this paper, we propose a zero-inflated bivariate generalized linear mixed model (ZIBGLMM) to tackle this issue. This two-component finite mixture model includes zero-inflation for a subpopulation with negligible or extremely low risk. We develop both frequentist and Bayesian versions of ZIBGLMM and examine its performance in estimating risk ratios (RRs) against the bivariate generalized linear mixed model and conventional two-stage meta-analysis that excludes DZS. Through extensive simulation studies and real-world meta-analysis case studies, we demonstrate that ZIBGLMM outperforms the bivariate generalized linear mixed model and conventional two-stage meta-analysis that excludes DZS in estimating the true effect size with substantially less bias and comparable coverage probability.
双零事件研究（DZS）在荟萃分析中准确估计总体治疗效果方面构成了挑战。当前的方法，如连续性校正或排除DZS，被普遍采用，但这些临时方法可能会得出有偏差的结论。尽管标准的双变量广义线性混合模型可以处理DZS，但它未能解决DZS与其他研究之间潜在的系统性差异。在本文中，我们提出了一种零膨胀双变量广义线性混合模型（ZIBGLMM）来解决这个问题。这个两成分有限混合模型包括对风险可忽略不计或极低的亚群体的零膨胀。我们开发了ZIBGLMM的频率主义和贝叶斯版本，并将其在估计风险比（RRs）方面的性能与双变量广义线性混合模型以及排除DZS的传统两阶段荟萃分析进行了比较。通过广泛的模拟研究和实际荟萃分析案例研究，我们证明，在估计真实效应大小方面，ZIBGLMM比双变量广义线性混合模型和排除DZS的传统两阶段荟萃分析表现更好，偏差显著更小，覆盖概率相当。
