Schooling C Mary, Jones Heidi E
1Graduate School of Public Health and Health Policy, City University of New York, 55 West 125th St, New York, NY 10027 USA.
School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong Special Administrative Region China.
Emerg Themes Epidemiol. 2018 Aug 8;15:10. doi: 10.1186/s12982-018-0080-z. eCollection 2018.
In biomedical research much effort is thought to be wasted. Recommendations for improvement have largely focused on processes and procedures. Here, we additionally suggest less ambiguity concerning the questions addressed.
We clarify the distinction between two conflated concepts, prediction and explanation, both encompassed by the term "risk factor", and give methods and presentation appropriate for each.
Risk prediction studies use statistical techniques to generate contextually specific data-driven models requiring a representative sample that identify people at risk of health conditions efficiently (target populations for interventions). Risk prediction studies do not necessarily include causes (targets of intervention), but may include cheap and easy to measure surrogates or biomarkers of causes. Explanatory studies, ideally embedded within an informative model of reality, assess the role of causal factors which if targeted for interventions, are likely to improve outcomes. Predictive models allow identification of people or populations at elevated disease risk enabling targeting of proven interventions acting on causal factors. Explanatory models allow identification of causal factors to target across populations to prevent disease.
Ensuring a clear match of question to methods and interpretation will reduce research waste due to misinterpretation.
在生物医学研究中，很多努力被认为是浪费的。改进建议主要集中在流程和程序上。在此，我们还建议在研究问题上减少模糊性。
我们阐明了两个相互混淆的概念——预测和解释之间的区别，这两个概念都包含在“风险因素”一词中，并给出了适合每个概念的方法和呈现方式。
风险预测研究使用统计技术来生成基于特定背景、由数据驱动的模型，这需要一个具有代表性的样本，以便有效地识别有健康状况风险的人群（干预的目标人群）。风险预测研究不一定包括病因（干预的目标），但可能包括病因的廉价且易于测量的替代物或生物标志物。解释性研究理想情况下应嵌入一个信息丰富的现实模型中，评估因果因素的作用，若针对这些因素进行干预，可能会改善结果。预测模型能够识别疾病风险升高的人群，从而使针对因果因素的已证实干预措施有的放矢。解释性模型能够识别跨人群的可针对的因果因素以预防疾病。
确保问题与方法及解释明确匹配，将减少因误解导致的研究浪费。
