Statistical assessment of the prognostic and the predictive value of biomarkers-A biomarker assessment framework with applications to traumatic brain injury biomarker studies. Issue 1 (January 2023)

• Record Type: Journal Article
• Authors: Bantis, Leonidas E; Young, Kate J; Tsimikas, John V; Mosier, Brian R; Gajewski, Byron; Yeatts, Sharon; Martin, Renee L; Barsan, William; Silbergleit, Robert; Rockswold, Gaylan; Korley, Frederick K
• Is Part Of: Research methods in medicine & health sciences. Volume 4:Issue 1(2023)
• Page Start: 34
• Page End: 48
• Publication Date: 2023-01
• Subjects: Biomarker combinations -- predictive biomarker -- prognostic biomarker -- receiver operating characteristi -- sensitivity -- specificity -- longitudinal biomarker -- generalized estimating equation; Medicine -- Research -- Periodicals; Medical sciences -- Research -- Periodicals; 610.7205
• DOI: 10.1177/26320843221141056
• Languages: English
• ISSNs: 2632-0843

Abstract

Studies that investigate the performance of prognostic and predictive biomarkers are commonplace in medicine. Evaluating the performance of biomarkers is challenging in traumatic brain injury (TBI) and other conditions when both the time factor (i.e. time from injury to biomarker measurement) and different levels or doses of treatments are in play. Such factors need to be accounted for when assessing the biomarker's performance in relation to a clinical outcome. The Hyperbaric Oxygen in Brain Injury Treatment (HOBIT) trial, a phase II randomized control clinical trial seeks to determine the dose of hyperbaric oxygen therapy (HBOT) for treating severe TBI that has the highest likelihood of demonstrating efficacy in a phase III trial. Hyperbaric Oxygen in Brain Injury Treatment will study up to 200 participants with severe TBI. This paper discusses the statistical approaches to assess the prognostic and predictive performance of the biomarkers studied in this trial, where prognosis refers to the association between a biomarker and the clinical outcome while the predictiveness refers to the ability of the biomarker to identify patient subgroups that benefit from therapy. Analyses based on initial biomarker levels accounting for different levels of HBOT and other baseline clinical characteristics, and analyses of longitudinal changes in biomarker levels are discussed from a statistical point of view. Methods for combining biomarkers that are of complementary nature are also considered and the relevant algorithms are illustrated in detail along with an extensive simulation study that assesses the performance of the statistical methods. Even though the discussed approaches are motivated by the HOBIT trial, their applications are broader. They can be applied in studies assessing the predictiveness and prognostic ability of biomarkers in relation to a well-defined therapeutic intervention and clinical outcome.