方法对比

	Meta-analytic Dose-Response Analysis	广义最小二乘法 (GLS)
领域	流行病学	统计学
方法族	Process / pipeline	Regression model
起源年份	1992	1935
提出者	Sander Greenland & Matthew P. Longnecker	Alexander Craig Aitken
类型	Quantitative meta-analytic method	Linear estimator
开创性文献	Greenland, S., & Longnecker, M. P. (1992). Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. American Journal of Epidemiology, 135(11), 1301–1309.	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.
别名	dose-response meta-analysis, DRMA, pooled dose-response modeling, trend meta-analysis	GLS, Aitken estimator, EGLS, feasible GLS
相关	2	3
摘要	Meta-analytic dose-response analysis pools summary statistics from multiple epidemiological studies to characterize how disease risk changes across ordered levels of an exposure. Rather than comparing a single high-exposure group against a reference, it reconstructs a continuous or categorical exposure-risk curve across the full range of doses, providing far richer evidence about the shape and magnitude of an association than any single study can supply.	Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig Aitken in 1935, GLS achieves the Best Linear Unbiased Estimator (BLUE) under a general error covariance structure by weighting observations according to their precision, providing a theoretical bridge between OLS and modern linear mixed models.