Improving equivalent scores for clinical neuropsychology: a new method for regression model selection

Abstract

Equivalent Scores (ES) represent a statistical gold standard to obtain thresholds for clinical inference from normative data. The procedure to obtain ES requires a preliminary mandatory step: to perform a regression model selection to obtain adjusted scores that take into account age, education, and sex. The current article, starting from theoretical considerations, focuses on this step and proposes a new and improved regression model selection method. Results from data simulation show that the newly proposed method outperforms the current one on a wide range of simulation parameters and conditions, leading to better performance in classifying impaired or unimpaired performances, and more precise ES. The article is associated with an online app and R code to allow to easily apply the method to other normative data. This new model selection procedure can be easily incorporated also with other regression-based norm approaches.

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