Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Correlation and Causation01:27

Correlation and Causation

Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Correlation and Regression00:53

Correlation and Regression

In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:

You might also read

Related Articles

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

Sort by
Same author

Technical evaluation of commercially available homologous recombination deficiency assays using MyChoice CDx as reference in ovarian cancer.

Gynecologic oncology·2026
Same author

A Study to Investigate the Safety and Immunogenicity of Monovalent Omicron LP.8.1-Adapted BNT162b2 COVID-19 Vaccine in Adults ≥ 65 Years of Age and High-Risk Adults 18-64 Years of Age (Preliminary Results).

Vaccines·2026
Same author

Corrigendum to "Associations of Tissue Tumor Mutational Burden and Mutational Status With Clinical Outcomes With Pembrolizumab Plus Chemotherapy Versus Chemotherapy For Metastatic NSCLC [JTO Clinical and Research Reports Vol. 4 No. 1: 100431].

JTO clinical and research reports·2025
Same author

Safety and Immunogenicity of Monovalent Omicron KP.2-Adapted BNT162b2 COVID-19 Vaccine in Adults: Single-Arm Substudy from a Phase 2/3 Trial.

Infectious diseases and therapy·2025
Same author

BCG Revaccination for the Prevention of <i>Mycobacterium tuberculosis</i> Infection.

The New England journal of medicine·2025
Same author

Urinary Kidney Injury Biomarker Profiles in Healthy Individuals and After Nephrotoxic and Ischemic Injury.

Clinical pharmacology and therapeutics·2025

Related Experiment Video

Updated: May 27, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Evaluating correlation-based metric for surrogate marker qualification within a causal correlation framework.

Yue Wang1, Robin Mogg, Jared Lunceford

  • 1BeiGene(Beijing) Co., Ltd., No. 30 Science Park Rd, Zhong-Guan-Cun Life Science Park, Changping District, Beijing 102206, P R China. yue.wang@beigene.com

Biometrics
|November 9, 2011
PubMed
Summary

Correlation is often used for biomarker qualification, but this study introduces a causal framework. Interpreting correlation cautiously prevents misleading conclusions about biomarker associations and drug development efficacy.

More Related Videos

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
07:13

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Related Experiment Videos

Last Updated: May 27, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
07:13

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Area of Science:

  • Biostatistics
  • Clinical Pharmacology
  • Drug Development

Background:

  • Biomarkers are crucial for efficient therapeutic development, enabling earlier clinical decisions.
  • Correlation between biomarkers and clinical endpoints is commonly used as initial evidence for biomarker qualification.
  • Current practices often overlook that correlation does not imply causation, potentially leading to flawed conclusions.

Purpose of the Study:

  • To introduce a causal correlation framework for a more rigorous assessment of biomarker associations.
  • To highlight the limitations of traditional correlation estimates in biomarker qualification.
  • To provide methods for testing causal quantities in clinical trial designs.

Main Methods:

  • Definition of two distinct types of individual-level correlations within a causal framework.
  • Analysis of the composite nature of correlation estimates.
  • Application of causal inference principles to biomarker assessment.

Main Results:

  • Correlation estimates are composites that require careful interpretation to avoid misleading conclusions in biomarker qualification.
  • A significant correlation can be observed even without a true underlying association, underscoring the need for causal inference.
  • Causal quantities of interest are testable in crossover designs, with challenges noted for parallel group settings.

Conclusions:

  • The study advocates for a causal correlation framework to enhance the reliability of biomarker qualification.
  • Misinterpretation of correlation can lead to erroneous conclusions regarding biomarker utility in drug development.
  • Causal inference methods offer a more robust approach to evaluating biomarker-target relationships in clinical trials.