用于事件时间分布荟萃分析的多元波利亚树模型

IF 1.4 4区 数学 Q3 BIOLOGY
Biometrics Pub Date : 2024-10-03 DOI:10.1093/biomtc/ujae136
Giovanni Poli, Elena Fountzilas, Apostolia-Maria Tsimeridou, Peter Müller
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引用次数: 0

摘要

我们通过将波利亚树($\mbox{PT}$)先验扩展为一组概率度量$G_1,\dots ,G_n$的联合先验,为随机概率度量族开发了一种非参数贝叶斯先验,适用于具有事件时间结果的元分析。在元分析的应用中,$G_i$ 是研究 $i$ 特有的事件时间分布。所提出的模型通过为任何一对具有相似特征的研究引入更高的相关性,定义了对研究特定协变量的回归。通过在$\mbox{PT}$结构中为每个$G_i$的条件分裂概率引入分层先验,构建了所需的多元$\mbox{PT}$模型。分层先验用高斯过程先验取代了 PT 结构中拆分概率的独立贝塔先验,高斯过程先验用于所有研究的相应(logit)拆分概率。高斯过程以特定研究的协变量为索引,通过增加相似研究的相关性来引入所需的依赖性。所提议的结构的主要特点是(有条件的)共轭后验更新,以及通常报告的事件时间数据推断总结。癌症免疫疗法研究的荟萃分析激发了这一构造。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A multivariate Polya tree model for meta-analysis with event-time distributions.

We develop a nonparametric Bayesian prior for a family of random probability measures by extending the Polya tree ($\mbox{PT}$) prior to a joint prior for a set of probability measures $G_1,\dots ,G_n$, suitable for meta-analysis with event-time outcomes. In the application to meta-analysis, $G_i$ is the event-time distribution specific to study $i$. The proposed model defines a regression on study-specific covariates by introducing increased correlation for any pair of studies with similar characteristics. The desired multivariate $\mbox{PT}$ model is constructed by introducing a hierarchical prior on the conditional splitting probabilities in the $\mbox{PT}$ construction for each of the $G_i$. The hierarchical prior replaces the independent beta priors for the splitting probability in the PT construction with a Gaussian process prior for corresponding (logit) splitting probabilities across all studies. The Gaussian process is indexed by study-specific covariates, introducing the desired dependence with increased correlation for similar studies. The main feature of the proposed construction is (conditionally) conjugate posterior updating with commonly reported inference summaries for event-time data. The construction is motivated by a meta-analysis over cancer immunotherapy studies.

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来源期刊
Biometrics
Biometrics 生物-生物学
CiteScore
2.70
自引率
5.30%
发文量
178
审稿时长
4-8 weeks
期刊介绍: The International Biometric Society is an international society promoting the development and application of statistical and mathematical theory and methods in the biosciences, including agriculture, biomedical science and public health, ecology, environmental sciences, forestry, and allied disciplines. The Society welcomes as members statisticians, mathematicians, biological scientists, and others devoted to interdisciplinary efforts in advancing the collection and interpretation of information in the biosciences. The Society sponsors the biennial International Biometric Conference, held in sites throughout the world; through its National Groups and Regions, it also Society sponsors regional and local meetings.
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