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| 메타분석적 용량-반응 분석× | 일반화 최소제곱법 (GLS)× | |
|---|---|---|
| 분야≠ | 역학 | 통계학 |
| 계열≠ | Process / pipeline | Regression model |
| 기원 연도≠ | 1992 | 1935 |
| 창시자≠ | Sander Greenland & Matthew P. Longnecker | Alexander Craig Aitken |
| 유형≠ | Quantitative meta-analytic method | Linear estimator |
| 원전≠ | 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. DOI ↗ | Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48. DOI ↗ |
| 별칭≠ | dose-response meta-analysis, DRMA, pooled dose-response modeling, trend meta-analysis | GLS, Aitken estimator, EGLS, feasible GLS |
| 관련≠ | 2 | 3 |
| 요약≠ | 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. |
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