在暴露-反应分析中使用泊松与逻辑回归的观点：见解和考虑。

IF 3 3区医学 Q2 PHARMACOLOGY & PHARMACY

CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2025-08-12 DOI:10.1002/psp4.70100

Yiming Cheng, Ping Chen, Yan Li

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引用次数: 0

摘要

这一观点评估了泊松与逻辑回归在二元暴露-反应（ER）数据建模中的应用。通过对不同样本量、事件率和ER斜率的模拟研究，我们强调了每种方法的优势和局限性。我们的研究结果表明，泊松回归适用于低事件率，而逻辑回归在更广泛的情况下提供一致的性能。这些见解有助于指导模型选择并提高药物开发中ER分析的稳健性。

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A Perspective on the Use of Poisson Versus Logistic Regression in Exposure-Response Analysis: Insights and Considerations.

This perspective evaluates the use of Poisson versus logistic regression in modeling binary exposure-response (ER) data. Through simulation studies across varying sample sizes, event rates, and ER slopes, we highlight the strengths and limitations of each method. Our findings show that Poisson regression is suitable under low event rates, while logistic regression provides consistent performance across broader scenarios. These insights help guide model selection and improve the robustness of ER analyses in drug development.

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