The Use of Mathematical Modeling Studies for Evidence Synthesis and Guideline Development: A Glossary

Autor/inn/en	Porgo, Teegwendé V.; Norris, Susan L.; Salanti, Georgia; Johnson, Leigh F.; Simpson, Julie A.; Low, Nicola; Egger, Matthias; Althaus, Christian L.
Titel	The Use of Mathematical Modeling Studies for Evidence Synthesis and Guideline Development: A Glossary
Quelle	In: Research Synthesis Methods, 10 (2019) 1, S.125-133 (9 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Porgo, Teegwendé V.) ORCID (Norris, Susan L.) ORCID (Salanti, Georgia) ORCID (Simpson, Julie A.) ORCID (Low, Nicola) ORCID (Egger, Matthias) ORCID (Althaus, Christian L.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1333
Schlagwörter	Nachschlagewerk; Mathematical Models; Evidence; Glossaries; Medical Research; Epidemiology; Guidelines; Medicine; Public Health + Suchen Sie Ihr Suchwort? Mathematical model; Mathematisches Modell; Evidenz; Glossary; Glossar; Epidemiologie; Richtlinien; Medizin; Gesundheitswesen
Abstract	Mathematical modeling studies are increasingly recognised as an important tool for evidence synthesis and to inform clinical and public health decision-making, particularly when data from systematic reviews of primary studies do not adequately answer a research question. However, systematic reviewers and guideline developers may struggle with using the results of modeling studies, because, at least in part, of the lack of a common understanding of concepts and terminology between evidence synthesis experts and mathematical modellers. The use of a common terminology for modeling studies across different clinical and epidemiological research fields that span infectious and non-communicable diseases will help systematic reviewers and guideline developers with the understanding, characterisation, comparison, and use of mathematical modeling studies. This glossary explains key terms used in mathematical modeling studies that are particularly salient to evidence synthesis and knowledge translation in clinical medicine and public health. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01