贝叶斯分析的危险性及其应对方法
Dangers of Bayesian analyses and how to address them
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中文总结 AI 辅助
本文针对FDA支持贝叶斯方法背景下，指出贝叶斯分析对先验偏倚敏感的危险性，提出基于实证文献论证先验、参考分析及将先验转化为数据等安全措施，并主张其作为传统分析的补充而非替代。
中文摘要 AI 辅助
鉴于美国食品药品监督管理局（FDA）宣布支持在临床试验中使用贝叶斯方法，我们对贝叶斯分析存在的问题及解决方法进行了非技术性综述。我们重点关注的是贝叶斯结果对其先验分布中所嵌入偏倚的众所周知的敏感性。这种脆弱性要求特殊的安全特性，包括基于实证研究文献而非单一专家意见的、对信息性先验的详细论证。至关重要的是，先验所提供的信息应根据实际背景数据来看是现实的，而不仅仅是根据现有专家的判断。我们通过一个涵盖这些要点的教科书式临床试验示例来说明我们的建议。然后，我们使用该示例描述贝叶斯分析及其呈现的基本诊断程序和安全建议。这些包括排除信息性先验的参考分析，以及基于实证研究文献而非哲学论证的、对信息性先验的论证。我们还批评了一些违反我们现实性要求的常见先验。帮助判断先验的一种策略是将其转化为要添加到研究数据中的数据。这些先验数据的来源应以与研究数据来源相同的批判态度进行审查。我们展示了一些用于比较先验与数据信息的简单汇总方法，包括先验对数据的预测、方差比及其向有效样本量（ESS）的转化。最后，我们认为贝叶斯分析应被视为传统分析的扩展或补充，而非替代品，因为这种处理方式在从频率学派向贝叶斯分析的教学和实践中，将先验的构建和论证置于首要位置。
英文摘要
In light of the US FDA announcement supporting the use of Bayesian methods in clinical trials, we present a nontechnical review of problems with Bayesian analyses and methods to address them. Our focus is on the well-known sensitivities of Bayesian results to biases embedded in their prior distributions. This vulnerability calls for special safety features, including detailed justification of informative priors based on empirical-research literature rather than on singular expert opinions. Crucially, the information contributed by priors should be realistic in light of actual background data rather than merely judged so by available experts. We illustrate our recommendations with using a textbook clinical-trial example which covered these points. We then use the example describe basic diagnostic procedures and safety recommendations for Bayesian analyses and their presentation. These include reference analyses which exclude informative priors, and justification for informative priors based on empirical-research literature rather than on philosophical arguments. We also critique some common priors that violate our realism requirements. One strategy to aid judgments about priors is to translate them into data to be added to the study data. The source of these prior data should be examined with the same critical attitude as the source of study data. We illustrate some simple summary methods for comparing prior and data information, including prior predictions of data, variance ratios, and their translation to effective sample sizes (ESS). Finally, we argue that Bayesian analyses should be treated as extensions or supplements to conventional analyses, rather than replace them, for that treatment puts prior construction and justification at the fore of the transition from frequentist to Bayesian analyses in teaching and practice.
发表机构
- University of California, Los Angeles（加州大学洛杉矶分校）
- University of Oxford（牛津大学）
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