观察性队列研究的个体参与者数据荟萃分析中系统遗漏的混杂因素。
Systematically missing confounders in individual participant data meta-analysis of observational cohort studies.
作者信息
Jackson Dan, White Ian, Kostis J B, Wilson A C, Folsom A R, Wu K, Chambless L, Benderly M, Goldbourt U, Willeit J, Kiechl S, Yarnell J W G, Sweetnam P M, Elwood P C, Cushman M, Psaty B M, Tracy R P, Tybjaerg-Hansen A, Haverkate F, de Maat M P M, Thompson S G, Fowkes F G R, Lee A J, Smith F B, Salomaa V, Harald K, Rasi V, Vahtera E, Jousilahti P, D'Agostino R, Kannel W B, Wilson P W F, Tofler G, Levy D, Marchioli R, Valagussa F, Rosengren A, Wilhelmsen L, Lappas G, Eriksson H, Cremer P, Nagel D, Curb J D, Rodriguez B, Yano K, Salonen J T, Nyyssönen K, Tuomainen T-P, Hedblad B, Engström G, Berglund G, Loewel H, Koenig W, Hense H W, Meade T W, Cooper J A, De Stavola B, Knottenbelt C, Miller G J, Cooper J A, Bauer K A, Rosenberg R D, Sato S, Kitamura A, Naito Y, Iso H, Salomaa V, Harald K, Rasi V, Vahtera E, Jousilahti P, Palosuo T, Ducimetiere P, Amouyel P, Arveiler D, Evans A E, Ferrieres J, Juhan-Vague I, Bingham A, Schulte H, Assmann G, Cantin B, Lamarche B, Despres J-P, Dagenais G R, Tunstall-Pedoe H, Lowe G D O, Woodward M, Ben-Shlomo Y, Davey Smith G, Palmieri V, Yeh J L, Meade T W, Rudnicka A, Brennan P, Knottenbelt C, Cooper J A, Ridker P, Rodeghiero F, Tosetto A, Shepherd J, Lowe G D O, Ford I, Robertson M, Brunner E, Shipley M, Feskens E J M, Di Angelantonio E, Kaptoge S, Lewington S, Lowe G D O, Sarwar N, Thompson S G, Walker M, Watson S, White I R, Wood A M, Danesh J
出版信息
Stat Med. 2009 Apr 15;28(8):1218-37. doi: 10.1002/sim.3540.
One difficulty in performing meta-analyses of observational cohort studies is that the availability of confounders may vary between cohorts, so that some cohorts provide fully adjusted analyses while others only provide partially adjusted analyses. Commonly, analyses of the association between an exposure and disease either are restricted to cohorts with full confounder information, or use all cohorts but do not fully adjust for confounding. We propose using a bivariate random-effects meta-analysis model to use information from all available cohorts while still adjusting for all the potential confounders. Our method uses both the fully adjusted and the partially adjusted estimated effects in the cohorts with full confounder information, together with an estimate of their within-cohort correlation. The method is applied to estimate the association between fibrinogen level and coronary heart disease incidence using data from 154,012 participants in 31 cohorts
对观察性队列研究进行荟萃分析的一个困难在于，不同队列中混杂因素的可得性可能不同，因此一些队列提供了完全调整后的分析，而另一些队列仅提供了部分调整后的分析。通常，对暴露与疾病之间关联的分析要么局限于具有完整混杂因素信息的队列，要么使用所有队列但未对混杂因素进行充分调整。我们建议使用双变量随机效应荟萃分析模型，以利用所有可用队列的信息，同时仍对所有潜在的混杂因素进行调整。我们的方法使用具有完整混杂因素信息的队列中的完全调整和部分调整估计效应，以及它们在队列内的相关性估计。该方法应用于使用来自31个队列的154,012名参与者的数据来估计纤维蛋白原水平与冠心病发病率之间的关联。
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