In meta-analysis with continuous outcomes, the use of effect sizes based on the means is the most common. It is often found, however, that only the quantile summary measures are reported in some studies, and in certain scenarios, a meta-analysis of the quantiles themselves are of interest. We propose a novel density-based approach to support the implementation of a comprehensive meta-analysis, when only the quantile summary measures are reported. The proposed approach uses flexible quantile-based distributions and percentile matching to estimate the unknown parameters without making any prior assumptions about the underlying distributions. Using simulated and real data, we show that the proposed novel density-based approach works as well as or better than the widely-used methods in estimating the means using quantile summaries without assuming a distribution apriori, and provides a novel tool for distribution visualisations. In addition to this, we introduce quantile-based meta-analysis methods for situations where a comparison of quantiles between groups themselves are of interest and found to be more suitable. Using both real and simulated data, we also demonstrate the applicability of these quantile-based methods.

翻译：在连续结局的元分析中，基于均值的效应量使用最为普遍。然而，研究发现某些研究中仅报告了分位数汇总指标，且在特定情境下，对分位数本身进行元分析具有重要价值。我们提出了一种基于密度的新方法，以支持在仅报告分位数汇总指标时实施全面的元分析。该方法采用灵活的分位数分布和百分位匹配来估计未知参数，无需对基础分布做任何先验假设。通过模拟和真实数据，我们证明所提出的基于密度新方法在使用分位数汇总估计均值时，其表现与广泛使用的方法相当或更优，且无需预先假设分布，同时为分布可视化提供了一种新工具。此外，针对组间分位数比较本身具有研究意义且更为适用的场景，我们引入了基于分位数的元分析方法。通过真实与模拟数据，我们也验证了这些基于分位数方法的适用性。