变系数模型简介及其在医学研究领域中的应用

Introduction and application of varying-coefficient models in medical research

简要介绍变系数模型,并以变系数Cox模型为例,通过实际数据分析展示其在医学和公共卫生领域的应用,为相关研究提供方法学参考。实例基于某疾病预防控制中心部分慢病管理数据,拟合变系数Cox模型,探索高血压人群中体重指数(body mass index,BMI)与死亡风险之间的时变关联。结果显示,体重过低(BMI<18.5 kg/m^(2))与较高的死亡风险相关,但其关联程度随着随访时间的延长而逐渐减弱;超重(23 kg/m^(2)≤BMI<25 kg/m^(2))与较低的死亡风险相关,其关联程度随着随访时间的延长而逐渐减弱。变系数模型能够捕捉到暴露因素对于结局的影响如何随着其他变量的改变而变化,有助于更为全面地理解变量间的复杂关系,在医学和公共卫生研究中有很好的应用和推广价值。

This paper briefly introduced the varying coefficient model and used the varying coefficient Cox model as an example to demonstrate its application in the fields of medicine and public health through real data analysis,thereby provided methodological references for related research.The example was based on chronic disease management data from a Center for Disease Control and Prevention,fitting a varying coefficient Cox model to explore the time-varying association between body mass index(BMI)and mortality risk among a hypertensive population.The results showed that being underweight(BMI<18.5 kg/m^(2))was associated with a higher risk of mortality,but this association weakened over time;being overweight(23 kg/m^(2)≤BMI<25 kg/m^(2))was associated with a lower risk of mortality,and this association also weakened over time.The varying coefficient model captured how the impact of exposure factors on outcomes changed with other variables,helping to comprehensively understand the complex relationships between variables,and had significant application and promotion value in medical and public health research.