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

Regression Analysis

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:

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...

Regression Toward the Mean

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...

Quantifying and Rejecting Outliers: The Grubbs Test

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...

Friedman Two-way Analysis of Variance by Ranks

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...

Sampling Plans

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

You might also read

Related Articles

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

Sort by

Same author

OsPHR2-OsDIR55 Module Enhances Root Barrier Integrity and Rice Tolerance to Phosphate Deficiency.

Plant, cell & environment·2026

Same author

Immunomodulatory Effects of Wheat Peptides and a Novel Octapeptide (GFNDLGKR) in Immunosuppressed Zebrafish and Mouse Models.

Food science & nutrition·2026

Same author

Wheat Peptides Promote Recovery of the Enteric Nervous System via mAChR Activation to Relieve Constipation in Mice.

ACS omega·2026

Same author

Enhanced doubly robust estimation in clinical trials accounting for intercurrent events.

Journal of biopharmaceutical statistics·2026

Same author

Multi-ancestry transcriptome prediction with functionally informed variants in TOPMed MESA improves performance of transcriptome-wide association studies.

American journal of human genetics·2026

Same author

Integrating axis quantitative trait loci looks beyond cell types and offers insights into brain-related traits.

Nature communications·2025

Same journal

Predictor-Assisted Nonparametric Graphical Models With Multivariate Error-Prone Data.

Statistics in medicine·2026

Same journal

Optimizing Treatment Decision Estimation for Right-Censored Survival Data Through Parameter Transfer Learning.

Statistics in medicine·2026

Same journal

Latent Class Log-Linear Models for Estimating Diagnostic Test Accuracy Without a Gold Standard: A Simulation Study.

Statistics in medicine·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

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 16, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Set-regression with applications to subgroup analysis.

Ao Yuan¹, Lida Wang¹, Ming T Tan¹

¹Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington, District of Columbia, USA.

Statistics in Medicine

|October 21, 2021

Summary

This summary is machine-generated.

Introducing set-regression, a novel statistical method for identifying optimal patient subgroups in clinical trials and precision medicine. This approach directly targets subgroup analysis, improving accuracy over traditional regression models.

Keywords:

clinical trial nonparametric Bayes model set-regression subgroup analysis

More Related Videos

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Related Experiment Videos

Last Updated: Oct 16, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Area of Science:

Statistics
Biostatistics
Machine Learning

Background:

Classical regression models estimate conditional means but are indirect for subgroup identification.
Precision medicine and clinical trials require identifying specific patient subgroups for targeted treatments.
Existing methods like recursive partitioning and support vector machines have limitations in direct subgroup optimization.

Purpose of the Study:

To introduce and define a new statistical regression method: set-regression.
To address the limitations of classical regression in direct subgroup analysis.
To provide a versatile tool for identifying patient subgroups that benefit most from treatments.

Main Methods:

Developed a novel set-regression model defined as the conditional mean of the response given covariates within a specific set A.
Extended classical regression, recursive partitioning, and support vector machine approaches.
Utilized simulation studies to evaluate the method's performance.

Main Results:

Set-regression directly addresses subgroup analysis, unlike traditional methods.
The proposed method demonstrates increased accuracy in identifying subgroups.
Simulation studies confirm superior performance in finite samples.

Conclusions:

Set-regression is a versatile and accurate method for subgroup analysis.
The approach is particularly suitable for precision medicine and clinical trial optimization.
Set-regression offers improved performance and ease of use compared to existing techniques.