Linganisha mbinu

	Uchambuzi wa Meta-analytic wa Uhusiano kati ya Kipimo na Mwitikio	Mbinu ya Viwango Vidogo Vilivyopanuliwa (GLS)	Uchambuzi Meta wa Mtandao
Nyanja	Epidemiolojia	Takwimu	Usanisi wa Ushahidi
Familia	Process / pipeline	Regression model	Process / pipeline
Mwaka wa asili	1992	1935	2002
Mwanzilishi	Sander Greenland & Matthew P. Longnecker	Alexander Craig Aitken	Lumley (2002)
Aina	Quantitative meta-analytic method	Linear estimator	Method
Chanzo asilia	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.	Lumley, T. (2002). Network meta-analysis for indirect treatment comparisons. Statistics in Medicine, 21(16), 2313–2324.
Majina mbadala	dose-response meta-analysis, DRMA, pooled dose-response modeling, trend meta-analysis	GLS, Aitken estimator, EGLS, feasible GLS	Mixed Treatment Comparison, MTC, Indirect Comparison Meta-Analysis
Zinazohusiana	2	3	1
Muhtasari	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.	Network meta-analysis (NMA) is a systematic method for comparing multiple interventions simultaneously within a single analytical framework, incorporating both direct evidence (head-to-head trials) and indirect evidence (comparisons via common comparators). First formalized by Lumley in 2002, NMA allows researchers to rank treatments and quantify comparative effectiveness even when some treatment pairs have never been directly studied.