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BIOMARKER CHANGE-POINT ESTIMATION WITH RIGHT CENSORING IN LONGITUDINAL STUDIES
Xiaoying Tang1, Michael I Miller2, Laurent Younes2
1SYSU-CMU Joint Institute of Engineering, Sun Yat-Sen University, No. 132, East Waihuan Road, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P.R. China.
The Annals of Applied Statistics
|October 2, 2018
Summary
This study introduces a statistical model to pinpoint disease biomarker changes relative to disease onset, even when onset is unobserved. The method accurately estimates change points, as shown in Alzheimer's disease research.
Area of Science:
- Biostatistics
- Neurology
- Medical Research
Background:
- Disease progression often involves critical changes in biomarkers.
- Accurately timing these changes relative to disease manifestation is crucial for understanding disease dynamics.
- Right-censoring, where events are not fully observed, complicates the analysis of biomarker change points.
Purpose of the Study:
- To develop and validate a statistical two-phase regression model for estimating disease biomarker change points.
- To account for right-censoring in the timing of disease manifestation.
- To apply the model to Alzheimer's disease, specifically relating amygdalar atrophy to cognitive decline.
Main Methods:
- Utilized a statistical two-phase regression model.
- Employed maximum likelihood estimation for point estimation.
- Incorporated bootstrap validation methods for model assessment.
- Applied the model to estimate the change point of amygdalar atrophy in Alzheimer's disease.
Main Results:
- Developed effective point estimation methods for the proposed statistical model.
- Demonstrated the model's effectiveness through numerical simulations.
- Successfully estimated the change point for amygdalar atrophy in relation to cognitive manifestation in Alzheimer's disease.
Conclusions:
- The developed statistical model and estimation methods are effective for analyzing biomarker change points with right-censored disease onset.
- This approach provides valuable insights into the temporal dynamics of neurodegenerative diseases like Alzheimer's.
- The findings support the use of this model for understanding disease progression and biomarker changes in clinical research.
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