ABSTRACT:Biomedical advances in the past decade have aimed to capitalize on two movements that have dominated the research conversation: precision medicine and the ascent of big data. These emerging shifts have resulted in growing confidence that we can better characterize health, predict who will get ill and with what, develop new treatments which exploit genetic, metabolic, and other vulnerabilities in cancers and infectious agents, and tailor some of these treatments to match characteristics of the individual patient and their specific disease. However, we suggest that there are important cautions. Weaknesses in the data and the methods used to study them raise three potential concerns. First, any data collected, and analysis attempted, will have limited utility absent internal validity, unless fundamental issues of accurate and consistent measurement can be addressed. Second, lack of attention to external validity limits generalizability beyond the narrow (even if large) samples in hand, so that the utility of inference that can emerge from these approaches remains limited. Third, the proposed approaches seldom include consideration of ubiquitous forces that can determine whether observed associations are truly attributable to the innovation or to other, unmeasured forces. This essay discusses these limitations and explores how they can influence inference drawn from big data precision medicine science.

中文翻译：

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精准医学方法和人口健康：研究设计的关注和考虑

摘要：过去十年的生物医学进步旨在利用主导研究对话的两项运动：精准医学和大数据的兴起。这些新出现的转变使我们越来越有信心，我们可以更好地描述健康特征，预测谁会生病以及患什么病，开发利用癌症和传染源中的遗传、代谢和其他脆弱性的新疗法，并定制其中一些疗法以匹配个体患者的特征及其特定疾病。但是，我们建议有一些重要的注意事项。数据的弱点和用于研究它们的方法引起了三个潜在的担忧。首先，任何收集的数据和尝试的分析，在没有内部有效性的情况下，效用有限，除非能够解决准确和一致测量的基本问题。其次，缺乏对外部有效性的关注限制了手头狭窄（即使大）样本之外的泛化性，因此可以从这些方法中得出的推理效用仍然有限。第三，提议的方法很少包括考虑无处不在的力量，这些力量可以确定观察到的关联是否真正归因于创新或其他未测量的力量。本文讨论了这些局限性，并探讨了它们如何影响从大数据精准医学科学中得出的推论。因此，可以从这些方法中得出的推理效用仍然有限。第三，提议的方法很少包括考虑无处不在的力量，这些力量可以确定观察到的关联是否真正归因于创新或其他未测量的力量。本文讨论了这些局限性，并探讨了它们如何影响从大数据精准医学科学中得出的推论。因此，可以从这些方法中得出的推理效用仍然有限。第三，提议的方法很少包括考虑无处不在的力量，这些力量可以确定观察到的关联是否真正归因于创新或其他未测量的力量。本文讨论了这些局限性，并探讨了它们如何影响从大数据精准医学科学中得出的推论。