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

Purposes: To establish a normative range of MemTrax (MTx) metrics in the Chinese population.

Methods: The 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.

Results: 26,633 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.

Conclusions: GAMLSS 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.