Sec.2-Ch.1-Subsec.3：Fixed-Effect Dose–Response Modeling in R
Secondary Analysis and Risk Prediction in Classical Clinical and Epidemiological Studies
In epidemiology and clinical research, understanding the relationship between exposure dose and disease risk is a fundamental basis for developing public health strategies and clinical interventions. Traditional case–control or cohort studies often compare only the risk difference between a high-exposure group and a reference group, while ignoring the continuous effect of different dose levels on disease risk. This makes it difficult to quantify the fine-grained details of risk variation.
A typical fixed-effect dose–response analysis provides a methodological approach to address this limitation. By using regression models, the risk values corresponding to each dose level are integrated into a continuous dose–risk trend, enabling quantification of “the change in risk associated with each unit increase in dose.” In this type of analysis, it is assumed that the dose–effect relationship within the study is unique and fixed, with no interference from between-study heterogeneity. As a result, the model parameters accurately reflect the average effect of each dose group within that study.
This approach not only fully utilizes information from all dose groups—thereby improving statistical efficiency and trend detection—but also lays the foundation for subsequent dose–response meta-analyses.
I. Understanding Typical Fixed-Effect Dose–Response Analysis
Typical fixed-effect dose–response analysis and dose–response meta-analysis are closely related methodologically, yet differ clearly in scope and purpose.
