[Some reflections on evidenced-based medicine, precision medicine, and big data-based research]

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.