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

Journal Article (Journal Article)

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 disease progression was remarkably slower in female ε3 homozygotes compared with female ε4 carriers and males. Simulation of a clinical trial suggests patient recruitment using the information of precise disease time of each patient will decrease the sample size required for clinical trials.

Full Text

Duke Authors

Cited Authors

  • Ishida, T; Tokuda, K; Hisaka, A; Honma, M; Kijima, S; Takatoku, H; Iwatsubo, T; Moritoyo, T; Suzuki, H; Alzheimer's Disease Neuroimaging Initiative,

Published Date

  • February 2019

Published In

Volume / Issue

  • 105 / 2

Start / End Page

  • 436 - 447

PubMed ID

  • 29951994

Pubmed Central ID

  • PMC6617977

Electronic International Standard Serial Number (EISSN)

  • 1532-6535

Digital Object Identifier (DOI)

  • 10.1002/cpt.1166


  • eng

Conference Location

  • United States