Prediction of conversion to Alzheimer's disease with longitudinal measures and time-to-event data

Predicting the timing of Alzheimer's disease (AD) conversion for individuals with mild cognitive impairment (MCI) can be significantly improved by incorporating longitudinal change information of clinical and neuroimaging markers, in addition to baseline characteristics, according to projections made by investigators. In a new article, the research team describes how their novel statistical models found that longitudinal measurements of ADAS-Cog was the strongest predictor for AD progression and...