Meta-analyses of two-group studies that report median differences typically rely on methods that require, in addition to the median difference and sample size, summary measures of dispersion such as quartiles or ranges. Studies that do not report such statistics are often excluded from the meta-analysis. Existing two-stage approaches first estimate the asymptotic variance of the median difference within each study under parametric assumptions, and then combine these study-specific estimates to obtain the pooled median difference and its variance. We propose Direct Variance Estimation (DiVE), a method that directly estimates the variance of the pooled difference using only study-level median differences and their sample sizes. A comprehensive simulation study across a wide range of distributional scenarios shows that DiVE performs comparably to or better than conventional two-stage methods, with clear advantages when the number of studies is small. A re-analysis of published meta-analyses demonstrates that DiVE enables the inclusion of studies lacking dispersion statistics, leading to a more comprehensive and potentially less biased synthesis of evidence.

翻译：报告两组研究中位差的荟萃分析通常依赖于除中位差和样本量外还需要四分位数或极差等离散指标汇总统计量的方法。未报告此类统计量的研究常被排除在荟萃分析之外。现有两阶段方法先在各研究内基于参数假设估计中位差的渐近方差，然后合并这些研究特异性估计值以获得合并中位差及其方差。我们提出直接方差估计法(DiVE)，该方法仅利用研究水平中位差及样本量直接估计合并差的方差。涵盖广泛分布情景的全面模拟研究表明，DiVE的表现与传统两阶段方法相当或更优，在研究数量较少时具有明显优势。对已发表荟萃分析的再分析表明，DiVE能够纳入缺乏离散统计量的研究，从而获得更全面且潜在偏倚更低的证据综合。