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Updated: May 22, 2026
07:40
Preparation of Peripheral Blood Mononuclear Cell Pellets and Plasma from a Single Blood Draw at Clinical Trial Sites for Biomarker Analysis
Published on: March 20, 2021
Two-phase designs for biomarker studies when disease processes are under intermittent observation
1Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
Biometrics
|May 21, 2026
Summary
This study introduces efficient two-phase biomarker study designs for chronic diseases. Pseudo-score residual-dependent sampling enhances biomarker effect estimation in large cohorts.
Area of Science:
- Biostatistics
- Epidemiology
- Chronic Disease Research
Background:
- Multistate models are effective for chronic disease progression in large cohorts.
- Biospecimens are collected at baseline, with intermittent disease assessments.
- Budgetary limits often restrict biospecimen analysis in large studies.
Purpose of the Study:
- To design efficient two-phase biomarker studies under budget constraints.
- To investigate subsampling strategies for biomarker analysis.
- To optimize the estimation of biomarker effects on disease progression.
Main Methods:
- Utilized multistate models for chronic disease progression.
- Developed and evaluated two-phase study designs with subsampling.
- Compared likelihood, conditional likelihood, and estimating function analyses.
- Investigated pseudo-score residual-dependent sampling strategies.
Main Results:
- Pseudo-score residual-dependent sampling yields highly efficient maximum likelihood estimates.
- Demonstrated efficiency gains from specific subsampling strategies.
- Applied methods to a study of HLA-B27 and joint damage in psoriatic arthritis.
Conclusions:
- Efficient two-phase designs are crucial for biomarker studies in chronic diseases.
- Pseudo-score residual-dependent sampling is an effective strategy for biomarker analysis.
- The findings have implications for optimizing resource allocation in cohort studies.
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