Toward digitally screening and profiling AD: A GAMLSS approach of MemTrax in China

Abstract

AbstractPURPOSESTo establish a normative range of MemTrax (MTx) metrics in the Chinese population.METHODSThe correct response percentage (MTx‐%C) and mean response time (MTx‐RT) were obtained and the composite scores (MTx‐Cp) calculated. Generalized additive models for location, shape and scale (GAMLSS) were applied to create percentile curves and evaluate goodness of fit, and the speed‐accuracy trade‐off was investigated.RESULTS26,633 subjects, including 13,771 (51.71%) men participated in this study. Age‐ and education‐specific percentiles of the metrics were generated. Q tests and worm plots indicated adequate fit for models of MTx‐RT and MTx‐Cp. Models of MTx‐%C for the low and intermediate education fit acceptably, but not well enough for a high level of education. A significant speed‐accuracy trade‐off was observed for MTx‐%C from 72 to 94.CONCLUSIONSGAMLSS is a reliable method to generate smoothed age‐ and education‐specific percentile curves of MTx metrics, which may be adopted for mass screening and follow‐ups addressing Alzheimer's disease or other cognitive diseases.Highlights GAMLSS was applied to establish nonlinear percentile curves of cognitive decline. Subjects with a high level of education demonstrate a later onset and slower decline of cognition. Speed‐accuracy trade‐off effects were observed in a subgroup with moderate accuracy. MemTrax can be used as a mass‐screen instrument for active cognition health management advice.