Application of latent variable methods to the study of cognitive decline when tests change over time

Background: The way a construct is measured can differ across cohort study visits, complicating longitudinal comparisons. We demonstrated the use of factor analysis to link differing cognitive test batteries over visits to common metrics representing general cognitive performance, memory, executive functioning, and language. Methods: We used data from three visits (over 26 years) of the Atherosclerosis Risk in Communities Neurocognitive Study (N = 14,252). We allowed individual tests to contribute information differentially by race, an important factor to consider in cognitive aging. Using generalized estimating equations, we compared associations of diabetes with cognitive change using general and domain-specific factor scores versus averages of equally weighted standardized test scores. Results: Factor scores provided stronger associations with diabetes at the expense of greater variability around estimates (e.g., for general cognitive performance, -0.064 standard deviation units/year, standard error = 0.015, vs. -0.041 standard deviation units/year, standard error = 0.014), which is consistent with the notion that factor scores more explicitly address error in measuring assessed traits than averages of standardized tests. Conclusions: Factor analysis facilitates use of all available data when measures change over time, and further, it allows objective evaluation and correction for differential item functioning.