对不良事件的回归模型的概述 分析分析
Elsa Coz1,2,3,4, Mathieu Fauvernier5,6,7,8, Delphine Maucort-Boulch1,2,3,4
1Université de Lyon, 69000, Lyon, France.
Drug safety
|November 25, 2023
概括
在临床试验中分析不良事件需要强大的回归模型. 本综述讨论了选择合适的模型来比较不良事件,强调绝对风险,以便更好地解释和比较研究.
科学领域:
- 药物监督 药物监督 药物监督
- 生物统计学 生物统计学
- 临床试验方法论 临床试验方法论
背景情况:
- 临床试验中的不良事件分析由于数据的复杂性和缺乏专业培训,往往不够理想.
- 基于治疗等共变量的不良事件比较是药物安全数据分析中常见但具有挑战性的任务.
- 现有的方法经常与不良事件的多方面的性质扎,包括时间,复发和严重程度.
研究的目的:
- 提供适用于比较临床和观察性研究中不良事件的回归模型的概述.
- 根据不良事件的特征指导选择适当的统计模型.
- 突出展示绝对风险与相对影响一起呈现的重要性,以改善解释和验证.
主要方法:
- 对现有的回归建模技术进行不良事件比较的审查.
- 讨论适应慢性治疗和间歇性事件的建模方法.
- 强调纳入不良事件的多个维度 (时间,复发,严重程度).
主要成果:
- 回归模型为比较不良事件提供了一个框架,但模型选择至关重要.
- 为了在慢性治疗期间存在间流动事件时建模不良事件,需要进行调整.
- 从模型中获得的绝对风险的系统陈述对于解释和验证至关重要.
结论:
- 适当的回归建模对于药物安全性方面的最佳不良事件分析至关重要.
- 考虑事件特征和呈现绝对风险可以提高统计分析的实用性.
- 关于绝对风险的标准化报告将有助于更好地解释和比较研究.
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