MIMIC Models: from Neuroscience to Health Sciences
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Abstract
In medicine, situations are often encountered where regression (linear or logistic) is not feasible, due to latent constructs or factors that include several observed variables, since they try to reflect or estimate complex multidimensional phenomena. This limitation led to the search for alternative statistical methodologies, such as a subtype of Structural Equation Models called MIMIC (Multiple Indicators Multiple Causes) models, originally used in neurosciences. In this issue we explain what these models are, what they are used for, as well as the programs for their application, and the interpretation of results.
It is exemplified with a study on the quality of life of people who received a transplant, highlighting the graphical representation and the importance of parsimony in the models. These models allow the simultaneous inclusion of factors and single variables, solving complex causal relationships, making them useful tools in medical research.
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