Introducing Continuous Time Meta-Analysis (CoTiMA)
Introducing Continuous Time Meta-Analysis (CoTiMA)
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DOI：
10.1177/1094428119847277
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发表时间：
2020-10-01
影响因子：
9.5
通讯作者：
Cortina, Jose M.
中科院分区：
文献类型：
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作者：
Dormann, Christian;Guthier, Christina;Cortina, Jose M.
面板数据的荟萃分析特别适合揭示随时间推移而发展的现象，但现有的方法有限。没有直接的方法来汇总使用不同时间滞后和不同数量的波的主要小组研究的结果。我们引入连续时间荟萃分析（CoTiMA）作为一种基于参数的方法来对交叉滞后面板相关矩阵进行荟萃分析。即使研究内部和研究之间存在不同的时间滞后，CoTiMA 也可以使用两个或多个波来聚合研究。因此，CoTiMA 提供了给定时间滞后的交叉滞后效应的荟萃分析估计，而不管该时间滞后在初步研究中使用的频率如何。我们描述了 CoTiMA 的连续时间基础、其相对于离散时间、基于相关性的结构方程模型 (MASEM) 荟萃分析的优势，以及 CoTiMA 如何应用于面板研究的荟萃分析。然后用一个例子来说明该方法。我们还进行了蒙特卡罗模拟，证明在各种条件下基于时间类别的 MASEM 的偏差比 CoTiMA 的偏差更大。最后，我们讨论数据要求、悬而未决的问题和未来可能的扩展。
Meta-analysis of panel data is uniquely suited to uncovering phenomena that develop over time, but extant approaches are limited. There is no straightforward means of aggregating findings of primary panel studies that use different time lags and different numbers of waves. We introduce continuous time meta-analysis (CoTiMA) as a parameter-based approach to meta-analysis of cross-lagged panel correlation matrices. CoTiMA enables aggregation of studies using two or more waves even if there are varying time lags within and between studies. CoTiMA thus provides meta-analytic estimates of cross-lagged effects for a given time lag regardless of the frequency with which that time lag is used in primary studies. We describe the continuous time underpinnings of CoTiMA, its advantages over discrete-time, correlation-based meta-analysis of structural equation models (MASEM), and how CoTiMA would be applied to meta-analysis of panel studies. An example is then used to illustrate the approach. We also conducted Monte Carlo simulations demonstrating that bias is larger for time category-based MASEM than for CoTiMA under various conditions. Finally, we discuss data requirements, open questions, and possible future extensions.