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| Verkostopohjainen meta-analyysi× | Systemaattinen kirjallisuuskatsaus× | |
|---|---|---|
| Tieteenala | Scientometriikka | Scientometriikka |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 2002 (Lumley); refined 2008–2012 | 1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines) |
| Kehittäjä≠ | Thomas Lumley (statistical framework); Georgia Salanti (SUCRA and ranking methods) | Archie Cochrane (conceptual foundation); formalized by the Cochrane Collaboration (1993) and Barbara Kitchenham in software engineering (2004) |
| Tyyppi≠ | Quantitative evidence synthesis | Evidence synthesis methodology |
| Alkuperäislähde≠ | Lumley, T. (2002). Network meta-analysis for indirect treatment comparisons. Statistics in Medicine, 21(16), 2313–2324. DOI ↗ | Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University Technical Report TR/SE-0401. link ↗ |
| Rinnakkaisnimet | NMA, network meta-analysis, mixed-treatment comparison, multiple-treatments meta-analysis | SLR, systematic review, evidence synthesis review, structured literature review |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | Network-based Meta-analysis (NMA) extends conventional pairwise meta-analysis by simultaneously synthesizing evidence across a network of two or more competing treatments, including pairs that have never been compared head-to-head in a single trial. By combining direct and indirect evidence within a coherent statistical model, NMA produces relative effect estimates for all treatment pairs and generates a probabilistic ranking of which treatment performs best on the outcome of interest. | A systematic literature review (SLR) is a structured, reproducible method for identifying, appraising, and synthesizing all relevant studies on a research question. Unlike a narrative review, it follows an explicit, pre-specified protocol — from database search strings through inclusion criteria to data extraction — so that the process is transparent, auditable, and replicable by other researchers. It is widely used in medicine, education, software engineering, and the social sciences to produce the most comprehensive possible evidence base on a topic. |
| ScholarGateAineisto ↗ |
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