Over the last decade, biomarker research has identified potential biomarkers for the diagnosis, prognosis, and management of traumatic brain injury (TBI). Several cerebrospinal fluid (CSF) and serum biomarkers have shown promise in predicting long-term outcome after severe TBI. Despite this increased focus on identifying biomarkers for outcome prognostication after a severe TBI, several challenges still exist in effectively modeling the significant heterogeneity observed in TBIrelated

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... as well as the biomarker-outcome relationships. Biomarker data collected over time are usually summarized into single-point estimates (e.g., average or peak biomarker levels), which are, in turn, used to examine the relationships between biomarker levels and outcomes. Further, many biomarker studies to date have focused on the prediction power of biomarkers without controlling for potential clinical and demographic confounders that have been previously shown to affect long-term outcome. In this article, we demonstrate the application of a practical approach to delineate and describe distinct subpopulations having similar longitudinal biomarker profiles and to model the relationships between these biomarker profiles and outcomes while taking into account potential confounding factors. As an example, we demonstrate a group-based modeling technique to identify temporal S100 calcium-binding protein B (S100b) profiles, measured from CSF over the first week post-injury, in a sample of adult subjects with TBI, and we use multivariate logistic regression to show that the prediction power of S100b biomarker profiles can be superior to the prediction power of single-point estimates.