将激活函数引入细分回归模型,以解决干预的滞后效应
Xiangliang Zhang1,2, Kunpeng Wu1,2, Yan Pan1,2
1Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
BMC medical research methodology
|November 24, 2023
概括
新的优化细分回归模型 (OSR-ReLU,OSR-Sig) 准确地估计了公共卫生干预的长期影响,并考虑到干预滞后效应. 这些模型比经典的细分回归提供了更好的准确性和精度.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 间断时间序列 (ITS) 设计是评估公共卫生干预措施的强有力的方法.
- 一个重要的ITS限制是干预滞后效应的不充分处理.
研究的目的:
- 开发和评估用于中断时间序列分析的新方法,这些方法明确模拟干预滞后效应.
- 将激活函数 (ReLU,Sigmoid) 引入细分回归,以优化干预影响的建模.
主要方法:
- 建议的优化细分回归 (OSR) 模型:OSR-ReLU和OSR-Sig,将激活功能集成到经典细分回归 (CSR) 中.
- 在各种场景中模拟数据:干预影响 (正/负),滞后模式 (线性/非线性),滞后长度和结果变化.
- 使用偏差,平均相对误差 (MRE),平均平方误差 (MSE),95%置信区间 (CI) 宽度和CI覆盖率来评估模型性能.
主要成果:
- 与CSR不同的是,OSR-ReLU和OSR-Sig提供了大约公正的长期影响估计.
- 与CSR相比,优化模型显示出更高的准确性 (较低的MRE,MSE) 和精度 (更窄的CI宽度,更高的覆盖率).
- 随着滞后长度的增加,OSR模型的性能保持稳健.
结论:
- 优化的细分回归模型 (OSR-ReLU,OSR-Sig) 在中断时间序列分析中有效地解决滞后效应.
- 这些新型模型提供了更准确和精确的估计长期公共卫生干预影响.
- 激活函数的集成为中断时间序列方法论提供了一个有希望的进步.
更多相关视频
10:46A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
10.7K
09:38Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
Published on: November 14, 2017
14.9K
相关概念视频
01:25Strategies for Assessing and Addressing Confounding
102
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
102
01:52Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K