文章摘要
唐金陵,李立明.关于循证医学、精准医学和大数据研究的几点看法[J].中华流行病学杂志,2018,39(1):1-7
关于循证医学、精准医学和大数据研究的几点看法
Some reflections on evidenced-based medicine, precision medicine, and big data-based research
收稿日期:2017-10-10  出版日期:2018-01-13
DOI:10.3760/cma.j.issn.0254-6450.2018.01.001
中文关键词: 循证医学;精准医学;大数据;现实世界研究;流行病学方法
英文关键词: Evidence-based medicine;Precision medicine;Big data;Real-world research;Epidemiological methods
基金项目:
作者单位E-mail
唐金陵 999077 中国香港中文大学公共卫生及基层医疗学院 jltang@cuhk.edu.hk 
李立明 100191 北京大学公共卫生学院 lmlee@vip.163.com 
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中文摘要:
      循证医学仍是当今最好的医学实践模式。需要注意的是,证据本身不等于决策,决策还必须考虑现有资源和人们的价值取向。证据显示,绝大多数患者不会因使用降血压、降血脂、降血糖、抗癌药而预防重要并发症或死亡,说明现代医学的很多诊断和治疗都不精准,找到那几个为数不多的对治疗有反应的患者就成了现代医学的梦。精准医学应运而生,但它并不是新概念,也不等于孤注一掷的基因测序。精准医学依赖的大队列多因素研究由来已久,也不是新方法。医学一直在寻求精准,而且在人类认知的各个层面都有所建树,如疫苗和抗体、血型与输血、影像对病灶的定位以及白内障晶体替换手术。基因不是达到精准的唯一途径,只是提供了新的可能性。但是多数基因和疾病关联强度很低,说明基因精准指导防治的价值可能不大,利用大数据和其他预测因素是精准医学的必经之路。在使用大数据问题上,强调拥有总体、大样本、关联关系而淡化因果关系,是严重的误导。科学从来不会待考察了总体后才进行推论;研究需要的样本量恰恰与效果大小成反比;否定因果关系就是对流行病学科学原理和方法的否定,放弃了对真实性的保障,最终会导致防治的无效。因此,在确认疗效上,基于大数据的现实世界观察性结果不能取代随机对照试验的实验性证据。本文谨希望以怀疑和批评的方式,激发出精准医学和大数据蕴藏的真正潜力。
英文摘要:
      Evidence-based medicine remains the best paradigm for medical practice. However, evidence alone is not decisions; decisions must also consider resources available and the values of people. Evidence shows that most of those treated with blood pressure-lowering, cholesterol-lowering, glucose-lowering and anti-cancer drugs do not benefit from preventing severe complications such as cardiovascular events and deaths. This implies that diagnosis and treatment in modern medicine in many circumstances is imprecise. It has become a dream to identify and treat only those few who can respond to the treatment. Precision medicine has thus come into being. Precision medicine is however not a new idea and cannot rely solely on gene sequencing as it was initially proposed. Neither is the large cohort and multi-factorial approach a new idea; in fact it has been used widely since 1950s. Since its very beginning, medicine has never stopped in searching for more precise diagnostic and therapeutic methods and already made achievements at various levels of our understanding and knowledge, such as vaccine, blood transfusion, imaging, and cataract surgery. Genetic biotechnology is not the only path to precision but merely a new method. Most genes are found only weakly associated with disease and are thus unlikely to lead to great improvement in diagnostic and therapeutic precision. The traditional multi-factorial approach by embracing big data and incorporating genetic factors is probably the most realistic way ahead for precision medicine. Big data boasts of possession of the total population and large sample size and claims correlation can displace causation. They are serious misleading concepts. Science has never had to observe the totality in order to draw a valid conclusion; a large sample size is required only when the anticipated effect is small and clinically less meaningful; emphasis on correlation over causation is equivalent to rejection of the scientific principles and methods in epidemiology and a call to give up the assurance for validity in scientific research, which will inevitably lead to futile interventions. Furthermore, in proving the effectiveness of intervention, analyses of real-world big data cannot displace the role of randomized controlled trial. We expressed doubts and critiques in this article on precision medicine and big data, merely hoping to stimulate discussing on the true potentials of precision medicine and big data.
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