Meta-analysis of longitudinal studies
Meta-analysis of longitudinal studies
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DOI：
10.1177/1740774507083567
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发表时间：
2007-01-01
期刊：
影响因子：
2.7
通讯作者：
Caro, J. Jaime
中科院分区：
文献类型：
--
作者：
Ishak, K. Jack;Platt, Robert W.;Caro, J. Jaime
纵向研究通常报告在随访过程中不同时间的治疗或暴露效果的估计。这些研究的荟萃分析必须考虑从同一study.Purpose的效果估计之间的相关性，以描述和对比替代方法来处理相关性固有的纵向效果估计在meta analyses.Methods。线性混合效应模型可以通过多种方式解释相关性。我们考虑了三种选择：包括研究特异性随机效应、相关时间特异性随机效应或也允许相关研究内残差的一般多变量规范。数据从一个审查的影响，脑深部电刺激（DBS）在帕金森氏病患者的研究被用来说明这些模型的应用。结果进行对比，从一个天真的荟萃分析，其中的相关性被ignored.Results的数据包括46项研究，产生了82个估计DBS的影响，测量在3，6，12个月或以后植入的刺激器。考虑相关性的模型，特别是完整的多变量规格，提供了更好的拟合（较低的AIC），并产生了稍微更精确的效应估计。这在一定程度上是由于一项研究中的一个相对极端的观察结果，该研究在其他时间提供了类似的估计值，在朴素方法中，由于它被视为一个独立的观察结果，因此会产生更大的影响。局限性由于参数的真实值未知，不可能证实多变量方法的估计值一定更准确。分析模型可以很容易地扩展到解释纵向研究中效应之间的相关性。这些模型可以提供更好的拟合和可能更精确的汇总效应估计。
Background Longitudinal studies typically report estimates of the effect of a treatment or exposure at various times during the course of follow-up. Meta-analyses of these studies must account for correlations between effect estimates from the same study.Purpose To describe and contrast alternative approaches to handling correlations inherent to longitudinal effect estimates in meta-analyses.Methods. Linear mixed-effects models can account for correlations in a number of ways. We considered three alternatives: including study-specific random-effects, correlated time-specific random-effects or a general multivariate specification that also allows correlated within-study residuals. Data from a review of studies of the effect of deep-brain stimulation (DBS) in patients with Parkinson's disease are used to illustrate the application of these models. Results are contrasted with those from a naive meta-analysis in which the correlations are ignored.Results The data included 46 studies that yielded 82 estimates of the effect of DBS measured at 3, 6, 12 months or later after implantation of the stimulator. Models that accounted for correlations, particularly the full multivariate specification, provided better fit (lower AIC) and yielded slightly more precise effect estimates. This was in part due to a relatively extreme observation from a study that provided similar estimates at other times, which in the naive approach exerts greater influence since it is treated as an independent observation.Limitations Since the true values of the parameters are not known, it is impossible to confirm that estimates from the multivariate approach are necessarily more accurate.Conclusion Standard meta-analytic models can be readily extended to account for correlations between effects in longitudinal studies. These models may provide better fit and possibly more precise summary effect estimates.