背景:个体患者数据(IPD)荟萃分析允许探索异质性,并可以识别从干预(或暴露)中受益最大的亚组,比汇总数据的荟萃分析更成功。一阶段或两阶段IPD荟萃分析是可能的,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,然后使用标准荟萃分析方法进行汇总。然而,两种方法的综合比较,在实践中,缺乏。
方法:我们为许多模拟场景中的每一个生成了1000个数据集,涵盖了不同的IPD大小和不同水平(截距和暴露)的不同研究间方差(异质性)假设。还使用了许多不同假设的模拟设置,而我们评估了主要效应和交互效应的表现。性能是根据平均偏差进行评估的,平均误差,覆盖范围,和权力。
结果: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;和协变量的固定研究特定效应)是总体上表现最好的,尤其是在调查互动时。对于主要影响,各个模型的性能几乎相同,除非存在截距异质性,在这种情况下,完全指定的一阶段和两阶段模型表现更好。对于相互作用的影响,在两阶段模型始终优于两个完全指定的一阶段模型的情况下,模型之间的差异更大。
结论:应首选完全指定的单阶段模型(考虑潜在的暴露,拦截,and,可能,相互作用异质性),尤其是在调查互动时。如果遇到随机研究截距不收敛,应使用固定的特定研究截距一阶段模型。
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.