对于罕见事件中风险或率比率的随机效应分布的非参数估计与基于手臂和基于对比的方法进行元分析
Patarawan Sangnawakij1, Dankmar Böhning2, Heinz Holling3
1Department of Mathematics and Statistics, Thammasat University, Khlong Luang, Pathum Thani, Thailand.
Statistics in medicine
|December 19, 2023
概括
本研究介绍了两种非参数混合模型,用于对罕见事件数据的元分析. 基于对比度的模型显示出卓越的性能,特别是在嵌套设计中,用于估计效果异质性.
科学领域:
- 生物统计学 生物统计学
- 临床流行病学临床流行病学
- 医学统计 医学统计
背景情况:
- 临床试验和队列研究中的罕见事件带来了独特的统计挑战.
- 对罕见事件数据的元分析需要可靠的方法来估计效应异质性.
- 二进制应急表通常用于表示罕见事件数据.
研究的目的:
- 在罕见事件研究的元分析中研究风险比参数对效应异质性的估计.
- 为了比较两种基于概率的非参数混合方法:基于臂的模型和基于对比的模型.
- 通过模拟和真实世界的医疗数据来评估这些方法的性能.
主要方法:
- 使用了两种基于概率的非参数混合模型:基于手臂和基于对比度.
- 使用预期-最大化 (EM) 算法进行最大概率估计.
- 开发了一种新的初始值选择策略,使用基于分类概率的随机分配.
- 使用AIC和BIC等标准比较模型性能,并评估偏差和平均平方误差的估计器.
主要成果:
- 基于对比的非参数混合模型更适合嵌套治疗组设计,以AIC和BIC表示.
- 基于手臂的模型在基于手臂的设计下表现良好,但有时会被基于对比度的模型所超越.
- 与Poisson回归和DerSimonian-Laird混合模型相比,基于对比度的方法在估计效应异质性方面通常表现更好,特别是在较小的样本大小方面.
结论:
- 基于对比的非参数混合模型被推用于罕见事件的元分析,特别是在嵌套研究设计中.
- 模型的选择 (基于手臂或基于对比) 取决于研究设计.
- 在罕见事件中精确估计效果异质性,元分析对于可靠的临床试验解释至关重要.
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