Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types 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...

Bioequivalence Data: Statistical Interpretation

Bioequivalence Data: Statistical Interpretation

The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...

Comparing the Survival Analysis of Two or More Groups

Comparing 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...

Biostatistics: Overview

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Sante publique (Vandoeuvre-les-Nancy, France)·2026

Same author

Author Correction: Cyclosporine versus placebo pretreatment of brain-dead donors and kidney graft function (Cis-A-Rein trial): a multicenter, double-blind, randomized, controlled trial.

Intensive care medicine·2026

Same author

Cyclosporine versus placebo pretreatment of brain-dead donors and kidney graft function (Cis-A-Rein trial): a multicenter, double-blind, randomized, controlled trial.

Intensive care medicine·2026

Same author

Differential regulation of proteolytic pathways in the diaphragm during mechanical ventilation.

Respiratory medicine·2025

Same author

Bridging the Scientific Knowledge Gap and Reproducibility: A Survey of Provenance, Assertion and Evidence Ontologies.

Proceedings of the ... International World-Wide Web Conference. International WWW Conference·2025

Same author

Oxygen therapy and noninvasive respiratory supports in acute hypoxemic respiratory failure: a narrative review.

Annals of intensive care·2024

Search research articles

Related Experiment Video

Updated: Jun 15, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Statistical evaluation of a biomarker.

Patrick Ray¹, Yannick Le Manach, Bruno Riou

¹Department of Emergency Medicine and Surgery, Groupe Hospitalier Pitié-Salpêtrière, Paris, France.

|March 18, 2010

Summary

Biomarker studies often lack rigorous statistical and methodological standards, hindering reliable scientific interpretation. Improving these standards is crucial for accurate biomarker assessment in clinical practice.

More Related Videos

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

Published on: January 28, 2014

Related Experiment Videos

Last Updated: Jun 15, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

Published on: January 28, 2014

Area of Science:

Biostatistics
Biomarker Discovery
Clinical Trials

Background:

Biomarkers are essential tools for disease diagnosis, severity assessment, and guiding therapeutic interventions.
Standardization in randomized trial reporting has advanced, but biomarker study assessment lags behind.
Many biomarker studies suffer from methodological flaws and poor statistical analysis, compromising reproducibility.

Purpose of the Study:

To highlight critical issues in the statistical evaluation and reporting of biomarker studies.
To provide guidance for improving the quality and reliability of biomarker research.
To inform investigators, editors, reviewers, and readers on best practices for biomarker study interpretation.

Main Methods:

Discussion of common biostatistical and methodological pitfalls in biomarker research.
Identification of key areas for improvement in study design and reporting.
Review of principles for robust statistical analysis in biomarker assessment.

Main Results:

Biomarker studies frequently exhibit significant statistical and methodological weaknesses.
Lack of standardization impedes the reliable translation of biomarker findings into clinical application.
Current reporting practices often fail to provide a clear and reproducible scientific message.

Conclusions:

Enhancing the statistical rigor and methodological transparency of biomarker studies is imperative.
Adherence to improved standards will ensure the validity and reproducibility of biomarker research.
Awareness and implementation of these recommendations are essential for all stakeholders involved in biomarker development and utilization.