Abstract and Introduction
Interest in developing and using novel biomarkers in critical care and perioperative medicine is increasing. Biomarkers studies are often presented with flaws in the statistical analysis that preclude them from providing a scientifically valid and clinically relevant message for clinicians. To improve scientific rigor, the proper application and reporting of traditional and emerging statistical methods (e.g., machine learning) of biomarker studies is required. This Readers' Toolbox article aims to be a starting point to nonexpert readers and investigators to understand traditional and emerging research methods to assess biomarkers in critical care and perioperative medicine.
Biomarkers are increasingly used as personalized markers of diagnosis, in the assessment of disease severity or risk, and to prognosticate and guide clinical decisions.[1,2] Biomarkers exploring the cardiovascular system and kidneys, as well as inflammation, have proliferated in critical care and perioperative medicine. While existing guidelines are available to provide guidance on key information to report in a biomarker study, they do not explicitly provide guidance on appropriate statistical methods.[3–5] The use of inappropriate statistical methods for assessing the clinical value of biomarkers obfuscates any meaningful interpretation and usability of the study findings for clinicians.[1,2]
This article does not aim to be an exhaustive review of biostatistical and methodologic issues, but rather, to be a starting point for nonexpert readers and investigators to understand traditional and emerging research methods used to assess biomarkers in critical care and perioperative medicine. We provide toolboxes with reporting checklists to assist authors and readers in the use of these statistical methods.
Anesthesiology. 2020;134(1):15-25. © 2020 American Society of Anesthesiologists | Lippincott Williams & Wilkins