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Use of Biostatistical Models to Manage Replicate Error in Concussion Biomarker Research
Jason B Tabor1,2,3, Jean-Michel Galarneau1, Linden C Penner1,2,3
1Sport Injury Prevention Research Centre, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada.
JAMA Network Open
|October 23, 2023
Summary
Statistical modeling for sport-related concussion (SRC) biomarkers must account for replicate errors. Multilevel regression using all data provides more accurate insights into SRC biomarker variation and associations than methods excluding data points.
Area of Science:
- Neuroscience
- Biomarkers
- Sports Medicine
Background:
- Advancing sport-related concussion (SRC) research requires ultrasensitive detection of fluid biomarkers.
- Common statistical methods may overlook replicate errors and specimen exclusion, impacting data interpretation.
- Robust modeling is needed to understand sample variation and improve statistical inferences for SRC biomarkers.
Purpose of the Study:
- To evaluate the impact of replicate error on SRC biomarker interpretation.
- To compare different biostatistical modeling approaches for SRC biomarker analysis.
Main Methods:
- Cross-sectional study of 149 healthy youth athletes (ages 11-18).
- Assayed preinjury plasma biomarkers (GFAP, UCH-L1, NFL, t-tau, p-tau-181) in duplicate.
- Compared multilevel regression (all data) with single-level regression (means, and means excluding >20% CV) to assess associations with age, sex, and prior concussion.
Main Results:
- Wide limits of agreement observed for GFAP, UCH-L1, and t-tau.
- GFAP and UCH-L1 showed significant associations with sex in multilevel and mean-based regression.
- Excluding specimens with >20% CV altered sex associations for GFAP and UCH-L1, reducing precision.
Conclusions:
- Varying replicate agreement suggests means may not optimize precision for population values.
- Multilevel regression effectively captures replicate variation, providing more representative estimates.
- This approach avoids challenges of exclusion thresholds, enhancing SRC biomarker analysis.
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