Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
NIHR School for Primary Care Research, University of Manchester, Manchester, UK.
Res Synth Methods. 2018 Sep;9(3):417-430. doi: 10.1002/jrsm.1303. Epub 2018 Jun 21.
Individual patient data (IPD) meta-analysis allows for the exploration of heterogeneity and can identify subgroups that most benefit from an intervention (or exposure), much more successfully than meta-analysis of aggregate data. One-stage or two-stage IPD meta-analysis is possible, with the former using mixed-effects regression models and the latter obtaining study estimates through simpler regression models before aggregating using standard meta-analysis methodology. However, a comprehensive comparison of the two methods, in practice, is lacking.
We generated 1000 datasets for each of many simulation scenarios covering different IPD sizes and different between-study variance (heterogeneity) assumptions at various levels (intercept and exposure). Numerous simulation settings of different assumptions were also used, while we evaluated performance both on main effects and interaction effects. Performance was assessed on mean bias, mean error, coverage, and power.
Fully specified one-stage models (random study intercept or fixed study-specific intercept; random exposure effect; and fixed study-specific effects for covariate) were the best performers overall, especially when investigating interactions. For main effects, performance was almost identical across models unless intercept heterogeneity was present, in which case the fully specified one-stage and the two-stage models performed better. For interaction effects, differences across models were greater with the two-stage model consistently outperformed by the two fully specified one-stage models.
A fully specified one-stage model should be preferred (accounting for potential exposure, intercept, and, possibly, interaction heterogeneity), especially when investigating interactions. If non-convergence is encountered with a random study intercept, the fixed study-specific intercept one-stage model should be used instead.
个体患者数据（IPD）荟萃分析允许探索异质性，并能够确定最受益于干预措施（或暴露）的亚组，比汇总数据荟萃分析成功得多。可以进行单阶段或两阶段 IPD 荟萃分析，前者使用混合效应回归模型，后者通过更简单的回归模型获得研究估计值，然后使用标准荟萃分析方法进行汇总。然而，实际上缺乏对这两种方法的全面比较。
我们为许多模拟场景中的每个场景生成了 1000 个数据集，这些场景涵盖了不同的 IPD 大小和不同的研究间方差（异质性）假设。还使用了许多不同假设的模拟设置，同时评估了主要效应和交互效应的性能。性能评估包括平均偏差、平均误差、覆盖度和功效。
完全指定的单阶段模型（随机研究截距或固定研究特定截距；随机暴露效应；以及协变量的固定研究特定效应）总体上是表现最好的，尤其是在研究交互作用时。对于主要效应，除非存在截距异质性，否则所有模型的性能几乎相同，在这种情况下，完全指定的单阶段和两阶段模型表现更好。对于交互作用，不同模型之间的差异更大，两阶段模型始终优于两个完全指定的单阶段模型。
应优先选择完全指定的单阶段模型（考虑潜在的暴露、截距和可能的交互异质性），尤其是在研究交互作用时。如果遇到随机研究截距不收敛的情况，应使用固定研究特定截距的单阶段模型。
