使用Pumas进行药动学-药效学贝叶斯推断的从业者指南
A Practitioner's Guide to Bayesian Inference in Pharmacometrics using Pumas
AI总结：
本文为药动学-药效学从业者提供基于Pumas的贝叶斯推断完整教程，涵盖模型定义、先验选择、后验采样、诊断与模型比较等全流程，并通俗解释高级贝叶斯概念。
摘要：
本文为药动学-药效学（pharmacometrics）领域的贝叶斯实践者提供了一份使用Pumas工作流的全面教程。我们首先简要阐述在药动学-药效学中采用贝叶斯推断的动机，并指出现有软件的局限性以及Pumas如何解决这些问题。随后，我们通过代码片段和示例描述药动学-药效学标准贝叶斯工作流的所有步骤，包括：模型定义、先验选择、从后验分布采样、先验与后验模拟和预测、反事实模拟和预测、收敛诊断、可视化预测检验，以及最后使用交叉验证进行模型比较。最后，我们用通俗语言解释贝叶斯统计中许多高级概念背后的背景和直觉，涵盖用户在执行贝叶斯分析时需要牢记的许多重要思想和注意事项。本文介绍的许多算法、代码和思想广泛适用于临床研究和统计学习，但考虑到Pumas作为主要面向药动学-药效学从业者的软件性质，我们选择将讨论范围聚焦于药动学-药效学领域，以保持更明确的主题边界。
英文摘要：
This paper provides a comprehensive tutorial for Bayesian practitioners in pharmacometrics using Pumas workflows. We start by giving a brief motivation of Bayesian inference for pharmacometrics highlighting limitations in existing software that Pumas addresses. We then follow by a description of all the steps of a standard Bayesian workflow for pharmacometrics using code snippets and examples. This includes: model definition, prior selection, sampling from the posterior, prior and posterior simulations and predictions, counter-factual simulations and predictions, convergence diagnostics, visual predictive checks, and finally model comparison with cross-validation. Finally, the background and intuition behind many advanced concepts in Bayesian statistics are explained in simple language. This includes many important ideas and precautions that users need to keep in mind when performing Bayesian analysis. Many of the algorithms, codes, and ideas presented in this paper are highly applicable to clinical research and statistical learning at large but we chose to focus our discussions on pharmacometrics in this paper to have a narrower scope in mind and given the nature of Pumas as a software primarily for pharmacometricians.
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。
