在嵌套案例控制数据的多状态模型中预测过渡概率.
Yen Chang1, Anastasia Ivanova1, Demetrius Albanes2
1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
Biometrics
|December 22, 2025
概括
嵌套病例控制 (NCC) 采样有效地预测了多状态模型过渡概率. 新的逆概率权重 (IPW) 方法提高了资源有限的环境中复杂事件分析的效率.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 生存分析的分析.
背景情况:
- 多状态模型对于分析复杂,相互关联的生活事件至关重要.
- 嵌套案例控制 (NCC) 采样在资源有限的环境中使用,但限制了对多个事件的数据重复使用.
- 反向概率加权 (IPW) 提供了一个与NCC数据推断的替代方案,主要用于相对风险估计.
研究的目的:
- 扩展基于IPW的伪概率方法,用于预测一般多态模型中的过渡概率.
- 评估和提高IPW方法的效率,以进行过渡概率预测.
- 提出和验证新的,更有效的IPW方法.
主要方法:
- 开发了两种基于IPW的假概率方法,以提高过渡概率预测的效率.
- 第一个方法是使用队列级信息校准设计权重.
- 第二种方法联合模拟来自同一状态的过渡,推导出明确的差异估计.
主要成果:
- 模拟研究证实,两种拟议的IPW方法都显著提高了效率.
- 两种新方法的联合应用使得效率进一步显著提高.
- 使用前列腺癌,肺癌,结肠直肠癌和卵巢癌查试验的真实数据来说明方法.
结论:
- 拟议的IPW方法显著提高了使用NCC数据在多状态模型中过渡概率预测的效率.
- 这些新的方法克服了标准IPW方法的局限性,使资源有限的环境中能够进行更强大的分析.
- 这些发现为流行病学研究和涉及复杂事件轨迹的公共卫生研究提供了宝贵的工具.
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