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

Statistical Software for Data Analysis and Clinical Trials

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...

You might also read

Related Articles

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

Sort by

Same author

Examining heterogeneity in pathways between alcohol-related factors and drinking during topiramate pharmacotherapy: A person-specific analysis.

Drug and alcohol dependence·2026

Same author

The Effects of Personalized Feedback About ALDH2*2, Alcohol Use, and Associated Health Risks on Drinking Intention and Consumption: The Role of Self-Efficacy and Perceived Threat.

Alcohol, clinical & experimental research·2026

Same author

Examining the Impact of Trial Length on Detecting Medication Effects for Alcohol Use Disorder: A Meta-Regression Study.

Alcohol, clinical & experimental research·2026

Same author

Assessing Simultaneous Infection with Multiple Pathogens via Group Testing with Imperfect Multiplex Assays.

Journal of agricultural, biological, and environmental statistics·2026

Same author

Telehealth-Delivered Mindfulness-Based Intervention: Protocol for a Randomized Clinical Trial for Individuals With Alcohol Use Disorder.

JMIR research protocols·2026

Same author

Consumption of Reinforcing Solutions Engages Dynamic Activity of the Prelimbic Cortical Outputs.

bioRxiv : the preprint server for biology·2026

Same journal

A Unified Framework for Jointly modelling Response Times and Item Position Effects in Computer-Based Learning Assessments.

Multivariate behavioral research·2026

Same journal

Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions.

Multivariate behavioral research·2026

Same journal

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026

Same journal

Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population.

Multivariate behavioral research·2026

Same journal

betaselectr: Selective (and Proper) Standardization in Structural Equation Models.

Multivariate behavioral research·2026

Same journal

Exploring the Use of Multiple Imputation for Handling Missing Covariates in Meta-Regression with Dependent Effect Sizes.

Multivariate behavioral research·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 1, 2026

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

Published on: January 5, 2018

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

James W Baurley¹, Carolyn M Ervin¹, Katie Witkiewitz²

¹BioRealm LLC 19330 Rim of the World Dr, Monument, CO, USA.

Multivariate Behavioral Research

|June 30, 2026

Summary

This summary is machine-generated.

A new R package offers advanced Bayesian machine learning tools to analyze complex alcohol use disorder (AUD) data. It captures individual differences and drinking patterns, improving research accuracy and clinical insights.

Keywords:

Alcohol use disorder bayesian statistics individual heterogeneity machine learning mixed-effects models

More Related Videos

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Published on: June 23, 2023

Murine Drinking Models in the Development of Pharmacotherapies for Alcoholism: Drinking in the Dark and Two-bottle Choice

Murine Drinking Models in the Development of Pharmacotherapies for Alcoholism: Drinking in the Dark and Two-bottle Choice

Published on: January 7, 2019

Related Experiment Videos

Last Updated: Jul 1, 2026

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

Published on: January 5, 2018

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Published on: June 23, 2023

Murine Drinking Models in the Development of Pharmacotherapies for Alcoholism: Drinking in the Dark and Two-bottle Choice

Murine Drinking Models in the Development of Pharmacotherapies for Alcoholism: Drinking in the Dark and Two-bottle Choice

Published on: January 7, 2019

Area of Science:

Data Science
Computational Statistics
Addiction Research

Background:

Alcohol use disorder (AUD) research struggles with individual variability and temporal drinking patterns.
Traditional statistical and machine learning methods are inadequate for hierarchical, longitudinal AUD data.

Purpose of the Study:

To introduce a novel R package with 30 Bayesian machine learning functions tailored for alcohol use research.
To address challenges in capturing heterogeneity and temporal dynamics in drinking behaviors.

Main Methods:

Developed an R package with mixed-effects and time-trend extensions for various Bayesian models, including BART.
Applied the package to longitudinal data from the ABQDrinQ cohort and the COMBINE clinical trial.

Main Results:

Identified significant associations between concurrent substance use and alcohol consumption.
Revealed nonlinear age effects on drinking variability, peaking between ages 25-30.
Achieved high-accuracy daily alcohol consumption predictions (median correlation 0.82).

Conclusions:

The R package provides essential uncertainty quantification for AUD research and clinical practice.
The framework offers a balance between model complexity and interpretability for substance use research.
The methodology is applicable to broader substance use research challenges.