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An evidence-based approach to the creation of normative data: base rates of impaired scores within a brief neuropsychological battery argue for age corrections, but against corrections for medical conditions

Abstract Objective: We detail a new approach to the creation of normative data for neuropsychological tests. The traditional approach to normative data creation is to make demographic adjustments based on observations of correlations between single neuropsychological tests and selected demographic variables. We argue, however, that this does not describe the implications for clinical practice, such as increased likelihood of misclassification of cognitive impairment, nor does it elucidate the impact on decision-making with a neuropsychological battery. Method: We propose base rate analyses; specifically, differential base rates of impaired scores between theoretical and actual base rates as the basis for decisions to create demographic adjustments within normative data. Differential base rates empirically describe the potential clinical implications of failing to create an appropriate normative group. We demonstrate this approach with data from a short telephone-administered neuropsychological battery given to a large, neurologically healthy sample aged 45–85 years old. We explored whether adjustments for age and medical conditions were warranted based on differential base rates of spuriously impaired scores. Conclusions: Theoretical base rates underestimated the frequency of impaired scores in older adults and overestimated the frequency of impaired scores in younger adults, providing an evidence base for the creation of age-corrected normative data. In contrast, the number of medical conditions (numerous cardiovascular, hormonal, and metabolic conditions) was not related to differential base rates of impaired scores. Despite a small correlation between number of medical conditions and each neuropsychological variable, normative adjustments for number of medical conditions does not appear warranted. Implications for creation of normative data are discussed.