一个位置规模的联合模型,用于研究血压与竞争事件的时间依赖的具体变量之间的联系
Léonie Courcoul1, Christophe Tzourio1, Mark Woodward2,3
1Univ. Bordeaux, INSERM, Bordeaux Population Health, U1219, France.
Statistics in medicine
|September 5, 2025
概括
血压变化是心血管和脑血管疾病 (CVD) 的重要危险因素. 我们的新统计模型证实了这一联系,
科学领域:
- 生物统计学
- 流行病学
- 心血管研究
背景情况:
- 由于发病率和死亡率高,心血管疾病 (CVD) 构成了重大公共卫生挑战.
- 血压升高是已知的危险因素,新出现的证据表明血压变化也可能是一个独立的预测因素.
- 关于血压变化的现有研究往往存在方法上的局限性.
研究的目的:
- 提出并验证一种新的联合统计模型,用于分析标记和竞争事件的重复测量.
- 调查血压变化与心血管疾病和全因死亡风险之间的关联.
- 展示风险预测模型中考虑异质差异的重要性.
主要方法:
- 开发一个联合的位置尺度模型,其中包含一个混合模型,包括对象特定的,时间依赖的剩余差异和因果特定的比例强度模型,用于竞争事件.
- 该模型允许事件风险取决于标记轨迹的当前值和斜率及其方差.
- 通过使用R包实现并通过模拟验证的Marquardt-Levenberg算法进行最大概率估计.
主要成果:
- 对中风二次预防的大型临床试验的应用表明,个体血压变化与心血管疾病和死亡风险有关.
- 与不考虑异质差异的模型相比,拟议的模型显示了更好的适合性和预测准确性.
- 这些发现强调了将血压变化纳入风险评估的临床意义.
结论:
- 开发的联合模型有效地捕捉了标记变化和竞争风险之间的复杂关系.
- 血压变化被证实是心血管疾病和死亡的重要独立风险因素.
- 考虑异质差异对于准确的风险预测和心血管健康的临床决策至关重要.
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