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使用双变变系数进行竞争性风险建模,以了解COVID-19的动态影响
Wenbo Wu1, John D Kalbfleisch2, Jeremy M G Taylor2
1Division of Biostatistics, Department of Population Health, Division of Nephrology, Department of Medicine, Center for Data Science, New York University.
概括
随着COVID-19大流行,透患者受到重大影响,随着时间的推移,影响会变化. 一个新的统计模型揭示了这些患者再入院和死亡的复杂动态.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 公共卫生 公共卫生
背景情况:
- COVID-19大流行严重影响了需要透析的末期病患者.
- 初步分析显示,COVID-19对透析患者的结果的影响因时间的变化而变化.
- 现有的模型不足以捕捉这些复杂的动态.
研究的目的:
- 开发和验证一种新的统计模型,用于分析COVID-19大流行期间透析患者的竞争风险.
- 量化COVID-19对末期病患者再入院和死亡的动态影响.
- 评估COVID-19影响的时间变化,相对于离院后的时间和流行病的开始.
主要方法:
- 提出了使用张量-产物 B-splines 对竞争风险的双变变系数模型.
- 开发了一种近位牛顿算法,用于在大型医疗保险透析患者数据集上高效地适应模型.
- 实施基于差异的异性无otropic 惩罚和对模型稳定性和参数选择进行交叉验证.
主要成果:
- 拟议的模型有效地捕捉了COVID-19对透析患者结果的复杂,时间变化的影响.
- 假设测试证实了COVID-19影响与出院后的时间和流行病持续时间的显著变化.
- 模型的性能通过应用到医疗保险透析数据和模拟研究来验证.
结论:
- 开发的双变量变系数模型为分析大量患者群体的时间依赖风险提供了强大的框架.
- 这种方法提供了关键的洞察力,了解大流行对脆弱患者群体 (如透析患者) 的细微影响.
- 这些发现强调了需要动态统计方法来理解和管理慢性疾病人群中的健康危机.
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