Managing biological variation data: modern approaches for study design and clinical application

Area of Science:

• Clinical Laboratory Science
• Biostatistics
• Biomarker Discovery

Background:

• Clinical test result variability is traditionally attributed to pre-analytical, analytical, and biological variation (BV).
• Understanding biological variation is crucial for accurate interpretation of clinical test results.
• Recent advancements facilitate the generation, collation, and application of BV data.

Purpose of the Study:

• To provide a historical framework of biological variation (BV) concepts for clinical laboratory scientists.
• To highlight practical applications of BV data within the clinical laboratory.
• To discuss statistical concepts pertinent to BV data analysis.

Main Methods:

• Review of prospective/direct study designs for BV data generation, including new checklist recommendations.
• Exploration of retrospective/indirect study designs using data mining from cohort studies and laboratory information systems (LIS).
• Discussion of updated BV databases and their accessibility.

Main Results:

• Recent advances enable more robust generation and utilization of biological variation data.
• Both prospective and retrospective study designs offer viable approaches for estimating BV.
• Accessible BV databases are increasingly available for clinical laboratory applications.

Conclusions:

• Biological variation is a critical, often underestimated, component of clinical test variability.
• Utilizing BV data enhances the interpretation and application of biomarker tests.
• This review equips scientists with knowledge to apply BV concepts and statistical methods effectively.