走向均值：一种实用的贝叶斯工作流，用于开发和部署临床预测模型
Progression to the mean: A comparison of Bayesian clinical prediction models outputting the posterior mean versus conventional plug-in predictions
AI总结：
本文提出了一种实用的贝叶斯工作流，用于开发和部署临床预测模型，通过使用收缩先验和个体后验均值决策方法，提高了预测性能和不确定性量化。
摘要：
临床预测模型为每个人提供预测（例如，估计风险），通常以点估计形式表达，来源于确定性函数如逻辑回归方程。此类'插件'预测隐藏了内在的不确定性。相比之下，贝叶斯方法提供了一种基于个体特定后验风险分布的不确定性量化机制。然而，由于感知的主观性、计算成本和实施复杂性，贝叶斯预测模型使用率较低。为此，我们提出了一种实用的贝叶斯流程，用于生成和部署预测模型。主要组成部分是（i）收缩先验，导致基于拉普拉斯/正态近似的回归系数后验分布，这避免了蒙特卡罗采样；以及（ii）使用个体的后验均值进行决策，这从期望效用视角得到支持。对于（i），我们建议具有互补特征的先验（简单性、用户输入、自动收缩）。对于（ii），我们建议计算后验均值的精确和近似方法，包括二次积分、麦克凯近似和投影预测映射的适应，从而创建一个简单的逻辑方程近似均值。通过示例和模拟，我们展示了贝叶斯工作流在预测性能上往往与插件预测相当或更好，同时能够通过合适覆盖的不确定性量化。在大多数模拟中，使用后验均值预测比插件预测在临床效用上更高，有时相当显著。总之，临床预测建模和部署的贝叶斯方法既实用又具有临床优势，因此高度推荐。
英文摘要：
Clinical prediction models provide predictions for individuals, typically expressed as point estimates derived from a deterministic function, such as a logistic regression equation. Such 'plug-in' predictions hide inherent uncertainty. In contrast, Bayesian methods offer a coherent mechanism for uncertainty propagation, and allow the computation of the posterior mean as the measure of centrality of choice for clinical decision-making. However, Bayesian methods are not widely utilised in predictive analytics for healthcare. We investigated the feasibility and performance of a Bayesian adaptation of the commonly used frequentist framework for risk prediction modelling. We assessed (i) the use of shrinkage priors with complementary features (simplicity, user input, and automatic shrinkage) that enable Laplace/normal approximation of the posterior, and (ii) exact and approximate methods for efficient computation of the posterior mean. Using examples and simulations, we demonstrate that this Bayesian approach is feasible and improves predictive performance, while enabling uncertainty quantification with suitable coverage. In small-to-medium sample sizes, the gain in clinical utility by using the posterior mean over plug-in predictions was equivalent to the gain from using a noticeably larger sample size. Adapting the widely used parametric regression methods to an approximate Bayesian framework for prediction modelling is both pragmatic and clinically advantageous.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
