Oct 10, 2015

The importance of prediction model validation and assessment in obesity and nutrition research

International Journal of Obesity : Journal of the International Association for the Study of Obesity
A E IvanescuD B Allison

Abstract

Deriving statistical models to predict one variable from one or more other variables, or predictive modeling, is an important activity in obesity and nutrition research. To determine the quality of the model, it is necessary to quantify and report the predictive validity of the derived models. Conducting validation of the predictive measures provides essential information to the research community about the model. Unfortunately, many articles fail to account for the nearly inevitable reduction in predictive ability that occurs when a model derived on one data set is applied to a new data set. Under some circumstances, the predictive validity can be reduced to nearly zero. In this overview, we explain why reductions in predictive validity occur, define the metrics commonly used to estimate the predictive validity of a model (for example, coefficient of determination (R(2)), mean squared error, sensitivity, specificity, receiver operating characteristic and concordance index) and describe methods to estimate the predictive validity (for example, cross-validation, bootstrap, and adjusted and shrunken R(2)). We emphasize that methods for estimating the expected reduction in predictive ability of a model in new samples are available...Continue Reading

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Mentioned in this Paper

Research
Medical Research Activity
Determination Aspects
Nutritional Study
Cross Validation
Evaluation
Obesity
Science of Nutrition
Two-Parameter Models
Reproducibility of Results

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