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Statistical models for longitudinal biomarkers of disease onset
1School of Operations Research and Industrial Engineering and Department of Statistical Science, Cornell University, Ithaca, NY 14853-3801, USA. slate@orie.cornell.edu
Statistics in Medicine
|March 1, 2000
Summary
This study introduces two novel statistical models for analyzing serial biomarker data to detect disease onset. These methods, applied to prostate-specific antigen (PSA) data, offer improved monitoring and screening for prostate cancer.
Area of Science:
- Biostatistics
- Medical Informatics
- Epidemiology
Background:
- Serial biomarker data are crucial for disease screening and monitoring but are often subject to measurement error.
- Accurate detection of disease onset is vital for timely intervention and improved patient outcomes.
- Existing methods may not fully account for the complexities of longitudinal biomarker data and error structures.
Purpose of the Study:
- To evaluate two recently proposed statistical models for analyzing serial biomarker data to detect disease onset.
- To develop dynamic indices reflecting the strength of evidence for disease onset over time.
- To compare the performance of these novel methods against standard diagnostic criteria using prostate-specific antigen (PSA) data.
Main Methods:
- A fully Bayesian hierarchical structure for a mixed-effects segmented regression model was employed, utilizing Gibbs sampling for posterior estimation of the changepoint (onset time) distribution.
- A hidden changepoint model was implemented, estimating the onset time distribution via maximum likelihood using the Expectation-Maximization (EM) algorithm.
- Both models generate a dynamic index to quantify the evidence of disease onset by the current time for individual subjects.
Main Results:
- The Bayesian and hidden changepoint models provide dynamic indices for assessing disease onset.
- Application to large prostate-specific antigen (PSA) datasets demonstrated the utility of these models for prostate cancer monitoring.
- Receiver Operating Characteristic (ROC) curves, adapted for longitudinal data, were used to compare index-based rules with standard diagnostic criteria.
Conclusions:
- The proposed Bayesian and hidden changepoint models offer robust frameworks for analyzing serial biomarkers and detecting disease onset.
- These dynamic indices provide a quantitative measure of evidence for disease onset, aiding in early detection and monitoring.
- The methods show promise for improving screening and diagnostic accuracy in longitudinal studies, as evidenced by PSA data analysis.
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