纵向等离子模算法用于在现实场景中评估统计方法:应用到职业流行病学中的示例
Youssra Souli1, Xavier Trudel2,3, Awa Diop2,3
1Institute for Stochastics Johannes Kepler University, Linz, Austria.
BMC medical research methodology
|October 18, 2023
概括
新的纵向等离子模拟使用真实数据来评估累积暴露的统计方法. 边缘结构模型 (MSM) 的表现优于标准回归,g计算显示出最佳结果.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 数据科学数据科学数据科学
背景情况:
- 等离子模拟使用真实数据生成合成数据来评估统计方法.
- 现有的等离子模式方法并不适用于纵向数据.
- 对许多健康研究来说,具有时间变化的暴露和共变量的纵向数据至关重要.
研究的目的:
- 提出一种新的纵向等离子模框架,用于生成现实的纵向数据.
- 为了比较不同统计方法在纵向数据中估计因果效应的性能.
- 评估估计工作中随时间累积暴露于心理社会压力因素的因果效应的方法.
主要方法:
- 开发了参数和非参数纵向等离子模算法.
- 利用了来自北克省PROspective (PROQ) 关于工作和健康的研究的数据.
- 在边际结构模型 (MSM) 中评估治疗权重的逆概率,g计算和目标最大概率估计,将它们与标准回归进行比较.
主要成果:
- 标准回归方法产生了偏差的估计和不充分的置信区间覆盖.
- 与标准回归相比,边际结构模型 (MSM) 显示偏差较低,覆盖率更好.
- 在偏差,RMSE和覆盖率方面,G计算在MSM估计者中通常表现最好,尽管在非参数模拟中没有一种方法是完美的.
结论:
- 纵向等离子模算法是评估分析方法在现实的模拟设置中有价值的工具.
- 建议使用边际结构模型 (MSM) 来估计累积风险投资的影响.
- 在GitHub上提供R函数,以方便使用这些新算法.
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