Collaborative, pooled and harmonized study designs for epidemiologic research: challenges and opportunities
Collaborative, pooled and harmonized study designs for epidemiologic research: challenges and opportunities
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DOI：
10.1093/ije/dyx283
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发表时间：
2018-04-01
影响因子：
7.7
通讯作者：
Lau, Bryan
中科院分区：
文献类型：
--
作者：
Lesko, Catherine R.;Jacobson, Lisa P.;Lau, Bryan
结合来自多个独立贡献研究（ICS）的个体水平数据的联合收割机合作研究设计（CSD）由于其许多优点而变得越来越普遍：通过大样本量增加统计功效;由于参与者的多样性而增加调查效应异质性的能力;通过利用现有数据实现成本效益;以及促进合作研究和培训初级研究者的能力。CSD还提出了可以克服的政治、后勤和方法挑战。数据统一可能会导致通用数据元素的减少，但仍有机会利用跨ICS的异构数据来调查测量误差和残余混杂。结合不同研究设计的数据是一门艺术，它促进了方法的发展。不同的研究样本，跨和内部的ICSs，提示从CSD的结果的普遍性的问题。然而，CSD提供了独特的机会，以一致的方式描述人群健康，地点和时间，并明确将结果推广到具有公共卫生利益的目标人群。在分析CSD数据时，还存在其他分析挑战，因为系统性偏差（例如信息偏差，混淆偏差）产生的机制可能因ICS而异，但多学科研究团队已准备好应对这些挑战。CSD是一个强大的工具，如果使用得当，可以进行以前不可能的研究。
Collaborative study designs (CSDs) that combine individual-level data from multiple independent contributing studies (ICSs) are becoming much more common due to their many advantages: increased statistical power through large sample sizes; increased ability to investigate effect heterogeneity due to diversity of participants; cost-efficiency through capitalizing on existing data; and ability to foster cooperative research and training of junior investigators. CSDs also present surmountable political, logistical and methodological challenges. Data harmonization may result in a reduced set of common data elements, but opportunities exist to leverage heterogeneous data across ICSs to investigate measurement error and residual confounding. Combining data from different study designs is an art, which motivates methods development. Diverse study samples, both across and within ICSs, prompt questions about the generalizability of results from CSDs. However, CSDs present unique opportunities to describe population health across person, place and time in a consistent fashion, and to explicitly generalize results to target populations of public health interest. Additional analytic challenges exist when analysing CSD data, because mechanisms by which systematic biases (e.g. information bias, confounding bias) arise may vary across ICSs, but multidisciplinary research teams are ready to tackle these challenges. CSDs are a powerful tool that, when properly harnessed, permits research that was not previously possible.