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在贝叶斯网络元分析中通过变量权力简化小数多项式

Andre Verhoek¹, Mario Jnm Ouwens², Bart Heeg³

¹Unit of Global Health, Department of Health Sciences, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.

Journal of comparative effectiveness research

|December 9, 2025

概括

这项研究引入了一种新的贝叶斯分数多项式 (FP) 建模方法,其中估计了转换力,改善了模型的适应性,并简化了卫生技术评估中的生存分析的选择.

科学领域:

生物统计学生物统计学
卫生技术评估卫生技术评估
网络元分析网络元分析

背景情况:

分数多项式 (FP) 模型对于医疗技术评估 (HTA) 和网络元分析 (NMA) 中的生存分析至关重要.
目前的FP实现使用固定功率,限制灵活性,预测性能,并在贝叶斯设置中增加计算成本.
需要更具适应性和效率的FP建模方法.

研究的目的:

引入和评估一种新的贝叶斯式FP建模方法,其中转换功率被估计为连续参数.
提高模型灵活性,改善统计匹配,简化生存分析中的模型选择.
为了减少贝叶斯式FP模型中的计算负担和结构不确定性.

主要方法:

使用STAN实现了二次贝叶斯FP模型,从数据中估计时间转换权力 (p1,p2).
在三个瘤学NMA数据集 (肺癌,前列腺癌,乳腺癌) 中评估了模型性能.
使用视觉合适,留出一个信息标准 (LOOIC),根平均平方误差 (RMSE),生存估计和计算效率来评估性能.

主要成果:

与固定功率模型相比,可变功率FP模型在所有数据集中都显示出优越的统计匹配 (较低的LOOIC和RMSE).
使用可变功率模型,增量生存估计更加稳定和临床可信,特别是在复杂的危险动态下.
虽然单个运行时间略长,但可变功率模型通过最小化所需的模型配置来降低整体计算负担.

结论:

具有可变功率的贝叶斯式FP模型增强模型适合性和简化选择,减少结构不确定性.
这种数据驱动的转换功率估计提高了解释性和计算效率.
该方法产生了强大的生存预测,支持HTA和比较有效性研究的可靠决策.

关键词:

贝叶斯网络的元分析.计算效率的计算效率分数多项式的分数多项式.卫生技术评估卫生技术评估结构性的不确定性.生存额外推算的生存率.

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