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.
Highlights
Mathematical models are increasingly used to aid decision making in public health and clinical medicine.[1,2] The results of mathematical modelling studies can provide evidence when a systematic review of primary studies does not identify sufficient studies to draw conclusions or to support a recommendation in a guideline, or when the studies that are identified do not apply to the specific populations of interest or do not provide data on long term follow up or on relevant outcomes
Mathematical modelling studies are frequently used to synthesize evidence from multiple data sources to address a clinical or public health question not directly addressed by a primary study
Researchers who develop and analyse mathematical models have different theoretical and practical backgrounds from systematic reviewers, guideline developers and policy-makers, which can result in a lack of a common understanding of concepts and terminology
Summary
Mathematical models are increasingly used to aid decision making in public health and clinical medicine.[1,2] The results of mathematical modelling studies can provide evidence when a systematic review of primary studies does not identify sufficient studies to draw conclusions or to support a recommendation in a guideline, or when the studies that are identified do not apply to the specific populations of interest or do not provide data on long term follow up or on relevant outcomes. Researchers who develop and analyse mathematical models have different theoretical and practical backgrounds from systematic reviewers, guideline developers and policy-makers, which can result in a lack of a common understanding of concepts and terminology These communication issues might result either in not using the findings of mathematical modelling studies in evidence synthesis and to inform decision-making, or accepting these findings without critical assessment.[8] A glossary of commonly used terms in mathematical modelling studies that are relevant to evidence synthesis and to clinical and public health guideline development could improve the use of such studies. We will consider statistical models as a class of mathematical models that are often integrated into complex mathematical modelling studies to relate the model output to data through a statistical framework The goal of this glossary is to provide a common terminology for public health specialists who would like to incorporate the results of mathematical modelling studies in systematic reviews and in the development of guidelines. Data-driven models infer their results primarily from data, and are not driven by theory or assumptions that are not well supported
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