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A Novel Method to Estimate Long-Term Chronological Changes From Fragmented Observations in Disease Progression
Takaaki Ishida1, Keita Tokuda1, Akihiro Hisaka2
1Department of Pharmacy, Faculty of Medicine, The University of Tokyo Hospital, The University of Tokyo, Tokyo, Japan.
Clinical Pharmacology and Therapeutics
|June 29, 2018
Summary
This study introduces a new method to track chronic disease progression using fragmented patient data, enabling a clearer understanding of disease timelines. This approach can improve clinical trial efficiency by precisely identifying patient disease stages.
Area of Science:
- Biostatistics
- Neurodegenerative Diseases
- Clinical Trials
Background:
- Chronic disease progression is difficult to study due to limited observation durations and imprecise patient disease staging.
- Quantitative understanding of disease chronology is essential for effective treatment and research.
Purpose of the Study:
- To develop a novel method for reconstituting long-term disease progression from temporally fragmented data.
- To estimate individual "disease time" for each subject, enhancing disease staging accuracy.
- To apply this method to Alzheimer's disease to model its 20-year progression.
Main Methods:
- Extended nonlinear mixed-effects models to incorporate "disease time" estimation.
- Applied the developed method to sporadic Alzheimer's disease patient data.
- Conducted covariate analysis to identify factors influencing disease onset and progression.
Main Results:
- Successfully depicted Alzheimer's disease progression over 20 years.
- Identified earlier amyloid-β accumulation in male and female apolipoprotein E ε4 homozygotes.
- Observed slower disease progression in female ε3 homozygotes compared to female ε4 carriers and males.
Conclusions:
- The novel method accurately models long-term chronic disease progression from fragmented data.
- Precise "disease time" estimation can significantly reduce sample size requirements for clinical trials.
- Understanding disease chronology aids in identifying disease modifiers and optimizing trial recruitment.
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