Clarifying questions about "risk factors": predictors versus explanation.
Clarifying questions about "risk factors": predictors versus explanation.
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DOI：
10.1186/s12982-018-0080-z
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发表时间：
2018
影响因子：
2.3
通讯作者：
Jones HE
中科院分区：
文献类型：
--
作者：
Schooling CM;Jones HE
在生物医学研究中，许多努力被认为是浪费了。改进建议主要集中在流程和程序上。在这里，我们还建议所讨论的问题不要含糊不清。我们澄清了预测和解释这两个混合概念之间的区别，这两个概念都包含在术语“风险因素”中，并给出了适合每个概念的方法和演示。风险预测研究使用统计技术来生成特定背景的数据驱动模型，需要具有代表性的样本来有效识别处于健康状况风险的人（干预的目标人群）。风险预测研究不一定包括原因（干预目标），但可能包括廉价且易于测量的替代物或原因生物标志物。解释性研究最好嵌入现实的信息模型中，评估因果因素的作用，如果针对干预措施，可能会改善结果。预测模型可以识别疾病风险较高的人群，从而针对因果因素采取行之有效的干预措施。解释模型可以识别针对不同人群的因果因素以预防疾病。确保问题与方法和解释的明确匹配将减少由于误解而造成的研究浪费。
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.