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贝叶斯生物标记效应估计,用于结合来自多个生物标记研究的数据

Zhiwei Rong^1,2, Jiali Song¹, Fengyu Sun³

¹Department of Biostatistics, School of Public Health, Peking University, Beijing, China.

Journal of applied statistics

|March 16, 2026

概括

这项研究引入了一种新的贝叶斯生物标记聚合 (BBP) 方法,用于跨研究标准化生物标记数据. BBP方法提高了生物标志物-疾病关联分析的准确性,特别是在有噪音数据的情况下.

科学领域:

生物统计学生物统计学
生物标志物发现发现
流行病学流行病学

背景情况:

从多个研究中汇集数据,增加了用于生物标志物-疾病关联分析的统计能力.
生物标志物测量的研究间变异性需要在数据汇集之前进行标准化.
现有的方法可能无法充分处理未校准的生物标本测量.

研究的目的:

开发和评估一种新的贝叶斯生物标记聚合 (BBP) 方法,用于从不同研究来源汇总生物标记数据.
在聚合分析中考虑未观察到的参考测量.
将BBP方法的性能与流行的统计方法进行比较.

主要方法:

开发了一种新的贝叶斯生物标记聚合 (BBP) 方法.
采用了两层模型,包括研究和生物标本.
作为潜变量,对未重新测试的生物标本进行处理的参考测量.
将BBP与内部化,全校准,双阶段,原始和x-only方法进行比较.

主要成果:

与现有方法相比,BBP方法显示出更高的性能.
在数据噪声高,效果大小强的场景中,BBP方法的优势最为显著.
对人类表皮生长因子受体2 (HER2) 基因表达和乳腺癌风险的说明性分析证实了BBP的疗效.

结论:

拟议的贝叶斯生物标记聚合 (BBP) 方法为标准化和聚合生物标记数据提供了一个强大的方法.
BBP有效地处理研究间的变化和未观察到的测量,增强生物标志物-疾病关联研究.
这种方法为生物标志物研究中的元分析提供了有价值的工具,如HER2和乳腺癌的例子所示.

关键词:

贝叶斯语贝叶斯语贝叶斯语贝叶斯语生物标志物生物标志物生物标志物与疾病的关联多重研究多重研究聚合聚合聚合聚合聚合

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