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

Interpreting R Charts01:22

Interpreting R Charts

39
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
39
Introduction to R01:11

Introduction to R

204
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
204
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K
Variability: Analysis01:11

Variability: Analysis

115
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
115
Multiple Regression01:25

Multiple Regression

2.9K
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...
2.9K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

95
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...
95

You might also read

Related Articles

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

Sort by
Same author

Severe Hospitalization-Requiring Viral Infection With Influenza or COVID-19: Metabolic Pathway Analysis.

Open forum infectious diseases·2026
Same author

Single-cell Sequencing Unveils A Profibrotic Macrophage and Infiltrating Monocyte Niche in the Bronchoalveolar Lavage of Patients with Chronic Obstructive Pulmonary Disease.

American journal of respiratory and critical care medicine·2026
Same author

Joint clinical and molecular subtyping of COPD with variational autoencoders.

Nature communications·2026
Same author

Impact of Recurrent Hypoglycemia on Brain Glucose Transport Kinetics in Type 1 Diabetes.

The Journal of clinical endocrinology and metabolism·2026
Same author

Discriminative Performance and Clinical utility of COPD Exacerbation Categories for Predicting Future Exacerbations.

American journal of respiratory and critical care medicine·2026
Same author

Whole Genome Sequence Analysis of Weight Loss in 16 972 Participants With COPD Reveals Novel Risk Loci in DRAIC and RFX3.

Journal of cachexia, sarcopenia and muscle·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

Related Experiment Video

Updated: May 10, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.0K

Extensions of Heterogeneity in Integration and Prediction (HIP) With R Shiny Application.

Jessica Butts1, Leif Verace1, Christine Wendt2

  • 1Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota, USA.

Statistics in Medicine
|April 25, 2025
PubMed
Summary
This summary is machine-generated.

We extended the Heterogeneity in Integration and Prediction (HIP) method to analyze complex diseases with diverse data types and subgroups. Our new tools, including an R Shiny app, make this powerful approach accessible for identifying disease markers.

Keywords:
COPDintegrative analysismultimodalmulti‐omicsmulti‐view datasubgroup heterogeneity

More Related Videos

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:49

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

53
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

Related Experiment Videos

Last Updated: May 10, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.0K
Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:49

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

53
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Integrating multiple data views enhances understanding of complex diseases.
  • Subgroup heterogeneity (e.g., by sex, race) is common in complex diseases.
  • Existing methods like HIP integrate data but have limitations.

Purpose of the Study:

  • Extend HIP to accommodate multi-class, Poisson, and Zero-Inflated Poisson outcomes.
  • Develop user-friendly tools for broader accessibility.
  • Identify common and subgroup-specific disease markers.

Main Methods:

  • Proposed extensions to the HIP method for diverse outcome types.
  • Developed an R Shiny application for HIP.
  • Utilized an R-package for HIP implementation.

Main Results:

  • Successfully extended HIP to new data types.
  • Demonstrated the application of HIP for identifying sex-specific disease markers.
  • Identified potential novel genes and proteins associated with chronic obstructive pulmonary disease (COPD).

Conclusions:

  • The extended HIP method and associated tools facilitate multi-view data integration and subgroup analysis.
  • The user-friendly interface increases accessibility for researchers.
  • This approach aids in discovering disease-specific biomarkers.