Quantification of disease progression and dropout for Alzheimer's disease

摘要: This research aimed to quantitatively describe the natural progression of Alzheimer's disease (AD) based on ADAScog scores in patients with mild-to-moderate AD. ADAS-cog data from 10 placebo-controlled clinical trials including more than 2,400 up 72 weeks treatment were used. Different models describing time course evaluated. Patient characteristics potentially affect score changes assessed. Furthermore, patient dropout patterns characterized using parametric survival models. Covariate selection was performed identify risk factors associated a higher rate. mild-tomoderate AD receiving placebo best described by log-linear model, where intercept represents log-transformed at Week 10, slope is (i.e., increase score) log scale. Covariates influencing baseline and Mini Mental State Exam score. No covariates influenced slope. A log-normal model fit best. Baseline age found be significant predictors for dropout. both established. These set quantitative basis future trial design endpoint selection.