Tang J L, Li L M
School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR 999077, China.
School of Public Health, Peking University, Beijing 100191, China.
Zhonghua Liu Xing Bing Xue Za Zhi. 2018 Jan 10;39(1):1-7. doi: 10.3760/cma.j.issn.0254-6450.2018.01.001.
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.
循证医学仍然是医疗实践的最佳范式。然而，仅有证据并不能做出决策；决策还必须考虑可用资源和人们的价值观。有证据表明，大多数接受降压、降脂、降糖和抗癌药物治疗的人并未从预防心血管事件和死亡等严重并发症中获益。这意味着现代医学在许多情况下的诊断和治疗并不精确。识别并仅治疗那些对治疗有反应的少数人已成为一个梦想。精准医学由此应运而生。然而，精准医学并非一个新想法，也不能像最初提议的那样仅依赖基因测序。大规模队列研究和多因素方法也不是新想法；事实上，自20世纪50年代以来它就已被广泛使用。从一开始，医学就从未停止过寻找更精确的诊断和治疗方法，并在我们的理解和知识的各个层面都取得了成就，如疫苗、输血、成像和白内障手术。基因生物技术不是实现精准的唯一途径，而仅仅是一种新方法。大多数基因与疾病的关联很弱，因此不太可能在诊断和治疗精度上带来巨大提升。通过纳入大数据并结合遗传因素的传统多因素方法可能是精准医学最现实的前进方向。大数据号称拥有总体人群和大样本量，并声称相关性可以取代因果关系。这些都是严重误导人的概念。科学从来不必观察总体来得出有效结论；只有当预期效果很小且临床意义不大时才需要大样本量；强调相关性而非因果关系等同于拒绝流行病学的科学原理和方法，以及放弃对科学研究有效性的保证，这将不可避免地导致徒劳的干预。此外，在证明干预的有效性时，对真实世界大数据的分析无法取代随机对照试验的作用。我们在本文中对精准医学和大数据表达了怀疑和批评，只是希望激发对精准医学和大数据真正潜力的讨论。
