Biomarker Validation
Biomarker Validation - Related Videos
09:00
High Throughput Sequential ELISA for Validation of Biomarkers of Acute Graft-Versus-Host Disease
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Cited by 14 •
2012
High throughput validation of multiple candidate biomarkers can be performed by sequential ELISA in order to minimize freeze/thaw cycles and use of precious plasma samples. Here, we demonstrate how to sequentially perform ELISAs for six different validated plasma biomarkers1-3 of graft-versus-host disease (GVHD)4 on the same plasma sample.
08:29
Biomarkers in an Animal Model for Revealing Neural, Hematologic, and Behavioral Correlates of PTSD
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Cited by 29 •
2012
We describe a rat model of post traumatic stress disorder (PTSD) that reveals the persistent alterations in neuroendocrine function and the delayed long-term, exaggerated fear response, characteristic of PTSD patients. The animal model and methods described here are useful for correlating biomarkers in brain nuclei, which are mechanistic but cannot be measured in patients, with biomarkers in peripheral white blood cells, which can.
08:04
Soxhlet Extraction of Lipid Biomarkers from Sediment
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2023
Source: Laboratory of Jeff Salacup - University of Massachusetts Amherst Every lab needs standards that track the performance, accuracy, and precision of its instruments over time to ensure a measurement made today is the same as a measurement made a year from now (Figure 1). Because standards must test the performance of instruments over a long period of time, large volumes of the standards are often required. Many chemical standards can be purchased from retail scientific companies, like...
01:29
Reliability and Validity
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2020
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways. Unfortunately, being consistent in measurement does not necessarily mean that you have measured something correctly. To illustrate this concept, consider a kitchen...
08:28
An Overview of bGDGT Biomarker Analysis for Paleoclimatology
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2023
Source: Laboratory of Jeff Salacup - University of Massachusetts Amherst Throughout this series of videos, natural samples were extracted and purified in search of organic compounds, called biomarkers, that can relate information on climates and environments of the past. One of the samples analyzed was sediment. Sediments accumulate over geologic time in basins, depressions in the Earth into which sediment flows through the action of fluid (water or air), movement, and gravity. Two main types...
FAQs
Analytical performance asks whether an assay produces measurements that are accurate, precise, sensitive, specific, and reproducible. Clinical validity asks whether those measurements correspond reliably with a defined disease state, physiological process, treatment response, or risk in an appropriate population. Separating these layers prevents a technically reliable assay from being mistaken for a clinically useful biomarker.
Accuracy indicates how closely measurements reflect the intended characteristic, while precision describes consistency across measurements. Sensitivity and specificity help assess how well the assay identifies relevant biological states and distinguishes them from others. Reproducibility shows whether results remain dependable across repeated assessments, supporting confidence that observed findings reflect biology rather than measurement variability.
A biomarker’s meaning depends on the population in which it is measured and the outcome used for comparison. Testing against well-defined outcomes helps determine whether the signal reflects disease, risk, physiological change, or treatment response. Appropriate population selection therefore supports clinical validity and reduces uncertainty when interpreting results in medical research or care.
A promising signal may show an association with a biological state, yet still lack dependable analytical performance or a demonstrated relationship with a meaningful clinical outcome. Biomarker validation combines assay evaluation with outcome-based testing to determine whether the signal is sufficiently reliable and relevant for diagnosis, prognosis, treatment selection, patient stratification, or monitoring.
The process begins by evaluating the assay’s analytical performance, including accuracy, precision, sensitivity, specificity, and reproducibility. Measurements are then examined against well-defined outcomes in appropriate populations to establish clinical validity. Finally, the evidence can support standardized implementation across laboratories and healthcare settings, provided the biomarker demonstrates reliable clinical relevance.
Validated biomarkers can contribute to diagnosis, prognosis, patient stratification, treatment selection, and monitoring. In clinical research, they help reduce uncertainty by linking measurable biological characteristics with defined outcomes or treatment responses. In care settings, their value depends on the established reliability and clinical relevance of the measurements rather than on a molecular signal alone.
Standardized implementation helps ensure that biomarker measurements remain comparable and dependable across laboratories and healthcare settings. Without this consistency, differences in assay performance could complicate interpretation and weaken confidence in clinical findings. Demonstrating reproducibility and other analytical characteristics provides a foundation for applying validated biomarkers more consistently in research and medical practice.