Related Experiment Videos
From differences in means between cases and controls to risk stratification: a business plan for biomarker
Nicolas Wentzensen1, Sholom Wacholder
1Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland 20852-7234, USA.
Cancer Discovery
|January 10, 2013
Summary
Biomarker development can be assessed early using case-control data analysis. This approach helps determine clinical utility and guides research, preventing futile efforts in biomarker discovery and testing.
Area of Science:
- Biomarker research
- Translational medicine
- Clinical diagnostics
Background:
- Biomarker development is crucial for early disease detection and clinical benefit.
- Significant resources are often expended on biomarker development and clinical testing with uncertain outcomes.
- Early-stage assessment of biomarker potential is needed to optimize research efforts.
Purpose of the Study:
- To provide theoretical background and practical tools for evaluating biomarker candidates.
- To establish a quantitative link between biomarker levels and clinically meaningful risk measures.
- To enable researchers to assess the utility, necessary improvements, population specificity, and futility of biomarker development.
Main Methods:
- Quantitative analysis connecting biomarker levels in cases and controls to risk measures.
- Application of established statistical theory to biomarker evaluation.
- Development of an interactive spreadsheet tool for practical use by researchers.
Main Results:
- A framework is provided to quantitatively assess the potential clinical benefit of biomarkers.
- Researchers can determine if a biomarker test is useful, requires technical improvement, or is population-specific.
- The methods allow for early identification of futile biomarker development efforts.
Conclusions:
- Early critical analysis of case-control data can predict the translational success or failure of biomarkers.
- The presented theory and interactive spreadsheet empower biomarker researchers to evaluate candidates effectively.
- This approach aims to reduce futile biomarker development and premature clinical testing by enabling early-stage assessment.
Related Concept Videos
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
01:20Comparing the Survival Analysis of Two or More Groups
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
01:25Strategies for Assessing and Addressing Confounding
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
01:12Hazard Ratio
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial evaluating a...
For example, in a clinical trial evaluating a...
01:29Bias in Epidemiological Studies
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
01:15Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...