Сравнение методов

	Метарегрессия	Взвешенный метод наименьших квадратов (ВМНК)
Область	Метаанализ	Статистика
Семейство	Regression model	Regression model
Год появления	2002	1935
Автор метода	Simon Thompson & Julian Higgins	Alexander Craig Aitken
Тип	Weighted regression for effect-size heterogeneity	Weighted linear estimator
Основополагающий источник	Thompson, S. G., & Higgins, J. P. T. (2002). How should meta-regression analyses be undertaken and interpreted? Statistics in Medicine, 21(11), 1559–1573.	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.
Другие названия	Meta-Analytic Regression, Weighted Regression in Meta-Analysis, Moderator Analysis, Meta-regresyon	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares
Связанные	2	3
Сводка	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.	Weighted Least Squares is a generalization of Ordinary Least Squares (OLS) regression that assigns each observation a weight inversely proportional to its error variance, thereby down-weighting high-variance data points and up-weighting precise ones. Introduced in its general matrix form by Alexander Craig Aitken in 1935, WLS is the canonical remedy when heteroscedasticity is present and the error variance structure is known or can be reliably estimated.