A Novel Method to Estimate Long-Term Chronological Changes From Fragmented Observations in Disease Progression.

ABSTRACT: Clinical observations of patients with chronic diseases are often restricted in terms of duration. Therefore, obtaining a quantitative and comprehensive understanding of the chronology of chronic diseases is challenging, because of the inability to precisely estimate the patient's disease stage at the time point of observation. We developed a novel method to reconstitute long-term disease progression from temporally fragmented data by extending the nonlinear mixed-effects model to incorporate the estimation of "disease time" of each subject. Application of this method to sporadic Alzheimer's disease successfully depicted disease progression over 20 years. The covariate analysis revealed earlier onset of amyloid-β accumulation in male and female apolipoprotein E ε4 homozygotes, whereas dise

SUBMITTER: Ishida T 

PROVIDER: S-EPMC6617977 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature