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

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares the...
Regression Analysis01:11

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 Regression01:25

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...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...

You might also read

Related Articles

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

Sort by
Same author

Examination of Peer Interactions During Cooperative and Competitive Board Games Among Children with Attention-Deficit/Hyperactivity Disorder on and off Methylphenidate.

Research on child and adolescent psychopathology·2026
Same author

Reliability and Validity of the Therapy Attitude Inventory in Caregivers Receiving Internet-Delivered Parent Child Interaction Therapy for Young Children with Developmental Delay.

Journal of psychopathology and behavioral assessment·2025
Same author

An Application of Time Series Analysis to Single-Case Designs in an Intensive Behavioral Intervention for ADHD.

Journal of attention disorders·2025
Same author

Correction: Modeling the Mediating Effects of HIV-Related Stigma on the Associations Between Race/Ethnicity and Antiretroviral Therapy Adherence and Viral Suppression Among Diverse Racial and Ethnic Minority Women with HIV.

AIDS and behavior·2025
Same author

Modeling the Mediating Effects of HIV-Related Stigma on the Associations Between Race/Ethnicity and Antiretroviral Therapy Adherence and Viral Suppression Among Diverse Racial and Ethnic Minority Women with HIV.

AIDS and behavior·2025
Same author

Random forest analysis and lasso regression outperform traditional methods in identifying missing data auxiliary variables when the MAR mechanism is nonlinear (p.s. Stop using Little's MCAR test).

Behavior research methods·2024
Same journal

Design and Feasibility Trial of Interventions to Reduce Young Adult Alcohol Use with Communities That Care Coalitions.

Prevention science : the official journal of the Society for Prevention Research·2026
Same journal

Introduction to the Special Issue on Structural Approaches to Youth Violence Prevention: Addressing Racism and Discrimination.

Prevention science : the official journal of the Society for Prevention Research·2026
Same journal

A family-centered digital intervention for parents of at-risk middle-school students.

Prevention science : the official journal of the Society for Prevention Research·2026
Same journal

Understanding Risk and Protective Factors of Intimate Partner Violence: A Public Health and Social Advocacy Approach.

Prevention science : the official journal of the Society for Prevention Research·2026
Same journal

Reframing Substance Misuse Prevention: a RE-AIM Analysis of Federal Infrastructure and Future Directions.

Prevention science : the official journal of the Society for Prevention Research·2026
Same journal

Health Without Barriers: Community-Engaged Adaptation of a Whole Family-Inclusive Intensive Health Behavior and Lifestyle Program in Rural Colorado.

Prevention science : the official journal of the Society for Prevention Research·2026
See all related articles

Related Experiment Video

Updated: Jul 5, 2026

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

Tutorial: Using Random Forest Analysis to Identify Auxiliary Variables of Missing Data.

Stefany Coxe1,2, Amanda N Baraldi3, Timothy Hayes4

  • 1Biostatistics Shared Resource, Cedars-Sinai Cancer, Los Angeles, CA, USA. stefany.coxe@csmc.edu.

Prevention Science : the Official Journal of the Society for Prevention Research
|July 3, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning, specifically random forest analysis (RFA), effectively identifies auxiliary variables for missing data in prevention science. This tutorial guides researchers on using RFA to improve data analysis when information is incomplete.

Keywords:
Auxiliary variablesMissing dataRandom forestTutorial

Related Experiment Videos

Last Updated: Jul 5, 2026

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

Area of Science:

  • Methodology in Prevention Science
  • Statistical Modeling
  • Machine Learning Applications

Background:

  • Missing data is a significant challenge in prevention science research.
  • Traditional methods for handling missing data, like full information maximum likelihood and multiple imputation, require auxiliary variables.
  • Selecting appropriate auxiliary variables is crucial but often difficult.

Purpose of the Study:

  • To provide a tutorial on using Random Forest Analysis (RFA) to identify auxiliary variables for missing data.
  • To demonstrate how RFA can overcome limitations of traditional methods, especially with nonlinear missingness patterns.
  • To guide prevention researchers in applying machine learning for improved missing data handling.

Main Methods:

  • Utilized Random Forest Analysis (RFA), a machine learning technique, to identify correlates of missingness.
  • Employed variable importance measures from RFA to select the most relevant auxiliary variables.
  • Applied the methods to a real dataset with over 100 variables from 215 participants.

Main Results:

  • RFA successfully identified correlates of missingness, performing well across various missing at random patterns.
  • Variable importance from RFA enabled prioritization of key auxiliary variables.
  • The tutorial demonstrates a practical workflow for RFA application in missing data analysis.

Conclusions:

  • Random Forest Analysis (RFA) offers a powerful and accessible method for identifying auxiliary variables in prevention research.
  • RFA enhances the ability to address missing data, particularly when missingness is nonlinear.
  • This tutorial aims to lower the barrier for prevention scientists to adopt RFA for more robust data imputation and analysis.