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

Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
Comparing the Survival Analysis of Two or More Groups01:20

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

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 from...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Contingency Table01:29

Contingency Table

A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...

You might also read

Related Articles

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

Sort by
Same author

HIV/STI Prevention Strategies During COVID-19 Among PrEP-Eligible Cisgender Women in New York State: A Qualitative Analysis.

International journal of environmental research and public health·2026
Same author

How effective are community health workers in managing and preventing perinatal depression in sub-Saharan Africa? A systematic review of quantitative evidence.

Health policy and planning·2025
Same author

Integrating Mental Health Services Into Perinatal Care: Challenges and Opportunities.

Journal of multidisciplinary healthcare·2025
Same author

Sweet taste preference on snack choice, added sugars intake, and diet quality- a pilot study.

BMC nutrition·2025
Same author

Facilitators and barriers to contraception access and use for Hispanic American adolescent women: An integrative literature review.

PLOS global public health·2024
Same author

Study protocol: Examining sexual and reproductive health literacy in Mexican American young women using a positive deviance approach.

PloS one·2024

Related Experiment Video

Updated: Jul 20, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

A guide for multilevel modeling of dyadic data with binary outcomes using SAS PROC NLMIXED.

James M McMahon, Enrique R Pouget, Stephanie Tortu

    Computational Statistics & Data Analysis
    |August 24, 2006
    PubMed
    Summary

    This study introduces multilevel modeling for dyadic data to analyze individual and group influences on health outcomes. The methods were applied to predict viral hepatitis C infection in heterosexual couples.

    More Related Videos

    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

    Related Experiment Videos

    Last Updated: Jul 20, 2026

    Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
    06:55

    Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

    Published on: January 8, 2020

    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:

    • Social and health sciences
    • Biostatistics
    • Epidemiology

    Background:

    • Hierarchically structured data are common in social and health sciences, with individuals nested within groups.
    • Dyadic data, representing pairs of individuals, present unique analytical challenges due to interdependence and small sample sizes.
    • Multilevel analytic techniques offer a framework to address these challenges in dyadic research.

    Purpose of the Study:

    • To describe multilevel analyses for modeling individual- and dyad-level predictors of binary outcomes.
    • To illustrate the application of these techniques using SAS statistical software.
    • To examine predictors of viral hepatitis C infection within heterosexual couples.

    Main Methods:

    • Multilevel modeling was employed to analyze data with variation at both individual and dyadic levels.
    • SAS statistical software was used for implementing the described analytical techniques.
    • The study focused on binary outcomes, specifically viral hepatitis C infection.

    Main Results:

    • The application demonstrated the feasibility of using multilevel models for dyadic data analysis.
    • Individual and dyadic factors influencing viral hepatitis C infection were estimated.
    • The study identified specific predictors at both individual and couple levels.

    Conclusions:

    • Multilevel modeling provides a robust approach for analyzing complex dyadic data in health and social sciences.
    • This methodology can effectively disentangle individual and dyadic influences on health outcomes.
    • The findings contribute to understanding the transmission dynamics of viral hepatitis C in couples.