Department of Research, The Norwegian Air Ambulance Foundation, Oslo, Norway.
Faculty of Health Sciences, University of Stavanger, Stavanger, Norway.
Scand J Trauma Resusc Emerg Med. 2024 Sep 2;32(1):79. doi: 10.1186/s13049-024-01256-4.
Healthcare is awash with numbers, and figuring out what knowledge these numbers might hold is worthwhile in order to improve patient care. Numbers allow for objective mathematical analysis of the information at hand, but while mathematics is objective by design, our choice of mathematical approach in a given situation is not. In prehospital and critical care, numbers stem from a wide range of different sources and situations, be it experimental setups, observational data or data registries, and what constitutes a "good" statistical analysis can be unclear. A well-crafted statistical analysis can help us see things our eyes cannot, and find patterns where our brains come short, ultimately contributing to changing clinical practice and improving patient outcome. With increasingly more advanced research questions and research designs, traditional statistical approaches are often inadequate, and being able to properly merge statistical competence with clinical knowhow is essential in order to arrive at not only correct, but also valuable and usable research results. By marrying clinical knowhow with rigorous statistical analysis we can accelerate the field of prehospital and critical care.
医疗保健领域充满了数字，弄清楚这些数字可能蕴含的知识对于改善患者护理是有价值的。数字允许对手头信息进行客观的数学分析，但尽管数学在设计上是客观的，但我们在给定情况下选择的数学方法并不是客观的。在院前和危重病护理中，数字来自于广泛的不同来源和情况，无论是实验设置、观察数据还是数据登记，什么构成了“好”的统计分析可能并不清楚。精心制作的统计分析可以帮助我们看到眼睛看不到的东西，在大脑无法处理的地方找到模式，最终有助于改变临床实践并改善患者的预后。随着越来越多的高级研究问题和研究设计，传统的统计方法往往不够用，能够正确地将统计能力与临床知识结合起来对于得出不仅正确而且有价值和可用的研究结果至关重要。通过将临床知识与严格的统计分析相结合，我们可以加速院前和危重病护理领域的发展。
