Use of Biostatistical Models to Manage Replicate Error in Concussion Biomarker Research
Notice bibliographique
Résumé
Importance: Advancing research on fluid biomarkers associated with sport-related concussion (SRC) highlights the importance of detecting low concentrations using ultrasensitive platforms. However, common statistical practices may overlook replicate errors and specimen exclusion, emphasizing the need to explore robust modeling approaches that consider all available replicate data for comprehensive understanding of sample variation and statistical inferences. Objective: To evaluate the impact of replicate error and different biostatistical modeling approaches on SRC biomarker interpretation. Design, Setting, and Participants: This cross-sectional study within the Surveillance in High Schools to Reduce the Risk of Concussions and Their Consequences study used data from healthy youth athletes (ages 11-18 years) collected from 3 sites across Canada between September 2019 and November 2021. Data were analyzed from November 2022 to February 2023. Exposures: Demographic variables included age, sex, and self-reported history of previous concussion. Main Outcomes and Measures: Outcomes of interest were preinjury plasma glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), neurofilament-light (NFL), total tau (t-tau) and phosphorylated-tau-181 (p-tau-181) assayed in duplicate. Bland-Altman analysis determined the 95% limits of agreement (LOAs) for each biomarker. The impact of replicate error was explored using 3 biostatistical modeling approaches assessing the associations of age, sex, and previous concussion on biomarker concentrations: multilevel regression using all available replicate data, single-level regression using the means of replicate data, and single-level regression with replicate means, excluding specimens demonstrating more than 20% coefficient variation (CV). Results: The sample included 149 healthy youth athletes (78 [52%] male; mean [SD] age, 15.74 [1.41] years; 51 participants [34%] reporting ≥1 previous concussions). Wide 95% LOAs were observed for GFAP (-17.74 to 18.20 pg/mL), UCH-L1 (-13.80 to 14.77 pg/mL), and t-tau (65.27% to 150.03%). GFAP and UCH-L1 were significantly associated with sex in multilevel regression (GFAP: effect size, 15.65%; β = -0.17; 95% CI, -0.30 to -0.04]; P = .02; UCH-L1: effect size, 17.24%; β = -0.19; 95% CI, -0.36 to -0.02]; P = .03) and single-level regression using the means of replicate data (GFAP: effect size, 15.56%; β = -0.17; 95% CI, -0.30 to -0.03]; P = .02; UCH-L1: effect size, 18.02%; β = -0.20; 95% CI, -0.37 to -0.03]; P = .02); however, there was no association for UCH-L1 after excluding specimens demonstrating more than 20% CV. Excluding specimens demonstrating more than 20% CV resulted in decreased differences associated with sex in GFAP (effect size, 12.29%; β = -0.14; 95% CI, -0.273 to -0.004]; P = .04) and increased sex differences in UCH-L1 (effect size, 23.59%; β = -0.27; 95% CI, -0.55 to 0.01]; P = .06), with the widest 95% CIs (ie, least precision) found in UCH-L1. Conclusions and Relevance: In this cross-sectional study of healthy youth athletes, varying levels of agreement between SRC biomarker technical replicates suggested that means of measurements may not optimize precision for population values. Multilevel regression modeling demonstrated how incorporating all available biomarker data could capture replicate variation, avoiding challenges associated with means and percentage of CV exclusion thresholds to produce more representative estimates of association.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,341 | 0,566 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,008 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,003 | 0,007 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,007 | 0,006 |
| Intégrité de la recherche | 0,005 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».