Best practices for using biological data in psychological research

Area of Science:

• Biomarker research
• Psychiatric research
• Data science

Background:

• Advances in statistical analysis necessitate larger sample sizes, driving team science efforts.
• Biological data collection has shifted from small lab studies to large, high-dimensional cohort and multi-site studies.
• Large-scale consortium efforts require careful data pipeline management, addressing cost, standardization, and harmonization.

Purpose of the Study:

• To synthesize recommended best practices for standardizing peripheral biomarker collection in psychiatric research.
• To enhance data quality and facilitate cross-study comparisons.
• To advance individual and team biomarker research.

Main Methods:

• Review of best practices for peripheral biomarker collection (blood, saliva, urine).
• Consideration of methodological decisions for biomarker measurement (modality, assay type).
• Emphasis on standardization across data collection, storage, quality control, and analysis.

Main Results:

• Standardization is critical for analyzing biological data across multiple cohorts and time points.
• Methodological choices impact data quality, feasibility, and interpretability.
• Best practices address challenges like cost, standardization of procedures, and cross-site harmonization.

Conclusions:

• Implementing standardized peripheral biomarker collection is essential for psychiatric research.
• Standardization improves data reliability and comparability across studies.
• This approach supports the advancement of biomarker discovery and application.