在NONMEM中贝叶斯估计
Curtis K Johnston1, Timothy Waterhouse1, Matthew Wiens1
1Metrum Research Group, Tariffville, Connecticut, USA.
贝叶斯估计为药物开发提供了强大的解决方案. 本教程详细介绍了使用药理动力学建模软件NONMEM的贝叶斯模型开发,评估和先前选择.
科学领域:
- 药理计量学和计算生物学
- 药物开发和监管科学 药物开发和监管科学
背景情况:
- 贝叶斯估计是药物开发的有价值的统计方法.
- 它在药物开发中的应用尽管具有潜力,但仍未得到充分利用.
- 了解贝叶斯原则对于有效的基于模型的药物设计至关重要.
研究的目的:
- 提供关于贝叶斯模型开发,评估和预先选择的教程.
- 展示贝叶斯模型在药物开发中的实际应用.
- 为了突出贝叶斯方法的实用性,使用药理动力学 (PK) 模型的例子.
主要方法:
- 贝叶斯模型开发和评估的概要原则.
- 解释贝叶斯分析中预先选择的策略.
- 使用非线性混合效果建模软件NONMEM进行演示.
- 将贝叶斯模型应用于一个示例的药理动力学 (PK) 模型.
主要成果:
- 该教程有效地展示了贝叶斯模型的开发和评估.
- 展示了使用NONMEM的贝叶斯模型的实际实现.
- 该示例突出了贝叶斯原则对PK建模的应用.
结论:
- 贝叶斯估计是药物开发中的一个强大的,尽管未得到充分利用的工具.
- 本教程为应用贝叶斯方法提供了基本的理解.
- 有效地使用贝叶斯模型可以增强药物开发决策.
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