用PoissonERM对二进制结果进行自动化的波桑回归暴露-响应分析
Yuchen Wang1, Luke Fostvedt2, Jessica Wojciechowski3
1Pfizer Inc, South San Francisco, California, USA.
CPT: pharmacometrics & systems pharmacology
|August 1, 2024
概括
PoissonERM是一个新的R包,简化了对二进制结果的暴露-反应 (ER) 分析,自动化了对不良事件 (AE) 风险评估的报告. 它有助于理解剂量反应关系和预测事件率.
科学领域:
- 制药指标 (Pharmacometrics) 是一个指标.
- 生物统计学 生物统计学
- 计算生物学 计算生物学
背景情况:
- 暴露-反应 (ER) 分析对于理解暴露于物质和不良事件 (AE) 发生之间的关系至关重要.
- 使用通用线性模型 (GLM) 的传统方法对于全面的ER分析可能是复杂和耗时的.
- 需要精简的工具来自动分析和报告ER研究中的二元结果.
研究的目的:
- 推出PoissonERM,一个旨在半自动化二进制结果的ER分析的R包.
- 为了促进暴露指标和AE的发生率之间的关系的建立.
- 为生成包括预测在内的全面分析报告提供一个用户友好的工具.
主要方法:
- PoissonERM使用Poisson回归来对二进制结果进行ER分析.
- 该套件半自动化了这个过程,包括数据处理,模型开发和使用R标记生成报告.
- 它结合了灵活的建模选项,包括多个尺度转换和反向消除用于共变量选择,同时处理相关的共变量.
主要成果:
- PoissonERM生成了对曝光指标,共变量和AE计数的汇总表和数字.
- 该套件根据指定的标准 (p值或偏差) 选择最佳的暴露度量.
- 它可以使用外部数据预测事件发生率,帮助评估各种剂量方案.
结论:
- PoissonERM提供了一种简化和高效的方法,用于对二进制结果进行和报告ER分析.
- 该套件增强了对AE发生与暴露水平相关的理解.
- 它的预测能力支持有关暴露和剂量策略的知情决策.
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