So sánh phương pháp

	Phân tích gộp liều-đáp ứng	Hồi quy siêu phân tích
Lĩnh vực	Tổng hợp bằng chứng	Phân tích tổng hợp
Họ	Process / pipeline	Regression model
Năm ra đời	1992	2002
Người khởi xướng	Greenland & Longnecker (1992), Advanced by Orsini et al. (2012)	Simon Thompson & Julian Higgins
Loại	Method	Weighted regression for effect-size heterogeneity
Công trình gốc	Greenland, S., & Longnecker, M. P. (1992). Methods for trend estimation of environmental health risks, with application to exposure to contaminated groundwater. Statistics in Medicine, 11(14‐15), 1837–1847.	Thompson, S. G., & Higgins, J. P. T. (2002). How should meta-regression analyses be undertaken and interpreted? Statistics in Medicine, 21(11), 1559–1573.
Tên gọi khác	Dose-Response Relationship, Non-Linear Meta-Analysis, Dose-Effect Synthesis	Meta-Analytic Regression, Weighted Regression in Meta-Analysis, Moderator Analysis, Meta-regresyon
Liên quan	1	2
Tóm tắt	Dose-response meta-analysis is a specialized evidence synthesis method that models the relationship between exposure dose (or intensity, duration, quantity) and health outcome across multiple studies, assessing whether effects follow a linear trend, nonlinear curve, or threshold pattern. Pioneered by Greenland and Longnecker (1992) and refined by Orsini et al. (2012), dose-response meta-analysis answers critical questions like 'Does cardiovascular disease risk increase consistently with salt intake, or is there a threshold above which risk plateaus?' and 'Does the benefit of physical activity increase linearly with exercise duration, or do diminishing returns occur at high doses?' This method is essential for risk assessment, policy-setting on safe exposure limits, and optimizing treatment dosing.	Meta-regression is a statistical technique that extends conventional meta-analysis by regressing study-level effect sizes on one or more study characteristics (moderators) to explain between-study heterogeneity. Formalized by Thompson and Higgins in 2002, it uses weighted least squares — weighting each study by the inverse of its variance — within a mixed-effects framework, allowing researchers to identify which study features systematically account for variation in observed effects across the literature.