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首页> 外文期刊>Pharmacoepidemiology and drug safety >Combining evidence from multiple electronic health care databases: performances of one‐stage and two‐stage meta‐analysis in matched case‐control studies
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Combining evidence from multiple electronic health care databases: performances of one‐stage and two‐stage meta‐analysis in matched case‐control studies

机译：组合来自多种电子医疗数据库的证据：匹配案例控制研究中的单级和两级间分析的性能

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Abstract Purpose Clustering of patients in databases is usually ignored in one‐stage meta‐analysis of multi‐database studies using matched case‐control data. The aim of this study was to compare bias and efficiency of such a one‐stage meta‐analysis with a two‐stage meta‐analysis. Methods First, we compared the approaches by generating matched case‐control data under 5 simulated scenarios, built by varying: (1) the exposure‐outcome association; (2) its variability among databases; (3) the confounding strength of one covariate on this association; (4) its variability; and (5) the (heterogeneous) confounding strength of two covariates. Second, we made the same comparison using empirical data from the ARITMO project, a multiple database study investigating the risk of ventricular arrhythmia following the use of medications with arrhythmogenic potential. In our study, we specifically investigated the effect of current use of promethazine. Results Bias increased for one‐stage meta‐analysis with increasing (1) between‐database variance of exposure effect and (2) heterogeneous confounding generated by two covariates. The efficiency of one‐stage meta‐analysis was slightly lower than that of two‐stage meta‐analysis for the majority of investigated scenarios. Based on ARITMO data, there were no evident differences between one‐stage (OR?=?1.50, CI?=?[1.08; 2.08]) and two‐stage (OR?=?1.55, CI?=?[1.12; 2.16]) approaches. Conclusions When the effect of interest is heterogeneous, a one‐stage meta‐analysis ignoring clustering gives biased estimates. Two‐stage meta‐analysis generates estimates at least as accurate and precise as one‐stage meta‐analysis. However, in a study using small databases and rare exposures and/or outcomes, a correct one‐stage meta‐analysis becomes essential.
机译：摘要目的在使用匹配的案例控制数据的多数据库研究的一级荟萃分析中忽略了患者的植物。本研究的目的是通过两级荟萃分析比较如此一级荟萃分析的偏差和效率。方法首先，我们通过在5个模拟场景下生成匹配的案例控制数据来比较方法，通过不同的模拟场景，（1）曝光结果协会; （2）数据库之间的可变性; （3）这种协会的一个协变的混淆力量; （4）其变异性; （5）两次协变量的（异质）混淆强度。其次，我们使用来自Aritmo项目的经验数据进行了相同的比较，这是一种多数据库研究，调查使用患有心律失常潜力的药物后心室心律失常的风险。在我们的研究中，我们专门研究了目前使用丙约的效果。结果偏差增加了一种阶段元分析，随着暴露效应的增加（1） - 数据库差异与两次协变量产生的异质混淆之间的数据库差异增加（1）。一级荟萃分析的效率略低于大多数调查情景的两阶段元分析。在Aritmo数据的基础上，一级（或？= 1.50，CI？=？[1.08; 2.08]）和两级（或？=？1.55，CI？[1.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.12; 2.16 ]）方法。结论当感兴趣的效果是异质的时，忽略聚类的一级间分析给出了偏见的估计。两阶段元分析至少会产生准确和精确的估计，作为单阶段的荟萃分析。然而，在使用小型数据库和稀有暴露和/或结果的研究中，正确的单阶段元分析变得必不可少。

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