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

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...

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...

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:

Multiple Regression

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

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...

One-Way ANOVA

One-Way ANOVA

One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...

You might also read

Related Articles

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

Sort by

Same author

Comparison of Efficacy Between Lymphoplasmapheresis and Plasma Exchange in Myasthenic Crisis.

Muscle & nerve·2025

Same author

Heterocyclic Conjugated Polydentate Ligands Facilitate Interfacial Charge Transfer of Air-Processed Carbon-Based All Inorganic Perovskite Solar Cells with an Efficiency Over 15.

Small (Weinheim an der Bergstrasse, Germany)·2025

Same author

Biological Effects of Dietary Restriction on Alzheimer's Disease: Experimental and Clinical Investigations.

CNS neuroscience & therapeutics·2025

Same author

Pre- or post-chemotherapy: effect on PSMA uptake.

EJNMMI research·2025

Same author

Co-precipitation enrichment of Pb<sup>2+</sup> in water by ball-milling-enhanced diatomite: synergistic effects of crystal structure and surface activity.

Environmental geochemistry and health·2025

Same author

China's greenhouse gas budget during 2000-2023.

National science review·2025

Same journal

A SEQUENTIAL SIGNIFICANCE TEST FOR TREATMENT BY COVARIATE INTERACTIONS.

Statistica Sinica·2026

Same journal

DEFINING AND ESTIMATING PRINCIPAL STRATUM SPECIFIC NATURAL MEDIATION EFFECTS WITH SEMI-COMPETING RISKS DATA.

Statistica Sinica·2026

Same journal

Longitudinal Modeling of Rank-based Global Outcome.

Statistica Sinica·2026

Same journal

INTEGRATING INCOMPLETE DATA FOR MEDIATION ANALYSIS.

Statistica Sinica·2026

Same journal

COMMUNITY EXTRACTION OF NETWORK DATA UNDER STOCHASTIC BLOCK MODELS.

Statistica Sinica·2026

Same journal

STATISTICAL INFERENCE FOR MEAN FUNCTIONS OF COMPLEX 3D OBJECTS.

Statistica Sinica·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 11, 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

High-dimensional Subgroup Regression Analysis.

Fei Jiang^1,2, Lu Tian^1,2, Jian Kang^1,2

¹University of California at San Francesco, Stanford University.

Statistica Sinica

|August 14, 2025

Summary

This summary is machine-generated.

This study introduces a novel regression approach for identifying subject subgroups with distinct models. It effectively detects subgroup-defining predictors and associated features, improving subgroup analysis in complex datasets.

Keywords:

Adolescent Brain Cognitive Development Study Functional magnetic resonance imaging Group Lasso High-dimensional regressions Subgroup analysis

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Related Experiment Videos

Last Updated: Sep 11, 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

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Area of Science:

Statistics
Machine Learning
Biostatistics

Background:

Classical regression assumes a single model for all subjects.
Modern data collection reveals subgroups with unique regression parameters.
Existing methods struggle with identifying these subgroups and their specific models.

Purpose of the Study:

To develop a new method for subgroup analysis in regression modeling.
To simultaneously identify subgroup-defining variables and predictors associated with the response.
To handle heterogeneous associations across subgroups.

Main Methods:

Modeling the response-predictor relationship with interactions between primary and auxiliary variables.
Utilizing penalties for sparsity and group structures in regression coefficients.
Implementing simultaneous feature selection for both primary and auxiliary predictors.

Main Results:

The proposed method effectively models heterogeneous associations across subgroups.
It achieves simultaneous feature selection for relevant primary and auxiliary predictors.
Asymptotic guarantees for parameter and cluster estimation consistency are established.

Conclusions:

This approach offers a robust framework for subgroup analysis in regression.
It enhances understanding of complex data structures by identifying distinct subject groups.
The method is validated using functional magnetic resonance imaging data from a large adolescent study.