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

Sign Test for Matched Pairs

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...

You might also read

Related Articles

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

Sort by

Same author

Correction: The association between systemic lupus erythematosus and cognitive impairment or dementia: a meta-analysis.

Frontiers in immunology·2026

Same author

Exploring the impact of AI tools on filmmakers' creative satisfaction: the mediating role of creative efficiency and autonomy.

Frontiers in psychology·2026

Same author

Association of cancer antigen 15-3 with distant recurrence in immunohistochemically defined breast cancer subtypes in Canadian Cancer Trials Group MA.32.

JNCI cancer spectrum·2026

Same author

The association between systemic lupus erythematosus and cognitive impairment or dementia: a meta-analysis.

Frontiers in immunology·2026

Same author

Early-onset rice body synovitis of the knee in children under three years: a case series and review of the literature.

Archives of orthopaedic and trauma surgery·2026

Same author

A phase 1 study of R-GDP-venetoclax before autologous transplant in relapsed/refractory large B-cell lymphoma (CCTG LY.18).

Blood neoplasia·2026

Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026

Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026

Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026

Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026

Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026

Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 9, 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

Testing for treatment-biomarker interaction based on local partial-likelihood.

Yicong Liu¹, Wenyu Jiang¹, Bingshu E Chen¹

¹Department of Mathematic and Statistics, Queen's University, Kingston, ON, Canada, K7L 3N6.

Statistics in Medicine

|June 19, 2015

Summary

This summary is machine-generated.

This study introduces a new bootstrap test (LPLB) to analyze how treatment effects vary with biomarkers in clinical trials. It helps identify patient subgroups who benefit most from treatments, advancing personalized medicine.

Keywords:

bootstrap clinical trials nonparametric estimation survival analysis treatment-covariate interaction

More Related Videos

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Related Experiment Videos

Last Updated: Apr 9, 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

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Area of Science:

Biostatistics
Clinical Trial Analysis
Personalized Medicine

Background:

Patient responses to treatments can differ based on biomarker profiles.
Identifying these differences is crucial for personalized medicine and effective treatment strategies.

Purpose of the Study:

To develop a novel statistical method for assessing treatment-biomarker interactions in survival data.
To determine if treatment effects are constant across all patients or vary with biomarker levels.

Main Methods:

Proposed the local partial-likelihood bootstrap (LPLB) test for survival outcome data.
Utilized local partial-likelihood estimation (LPLE) and bootstrapped martingale residuals.
Developed a test statistic based on asymptotic martingale theories to quantify treatment effect variation.

Main Results:

The LPLB method is nonparametric and flexible.
Demonstrated superior power in detecting complex treatment-biomarker interactions compared to standard Cox models.
Successfully identified treatment effects within biomarker-defined subsets in simulation and real-world data.

Conclusions:

The LPLB test offers a powerful and flexible approach for analyzing treatment-biomarker interactions.
This method enhances the identification of patient subgroups benefiting from specific treatments.
Applicable to breast and prostate cancer clinical trial data, supporting personalized treatment strategies.