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

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

Clearance Models: Noncompartmental Models

Clearance Models: Noncompartmental Models

Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

Exponential Equations for Modeling Growth

Exponential Equations for Modeling Growth

Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is the relative...

Assumptions of Survival Analysis

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.

Longitudinal Studies

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...

You might also read

Related Articles

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

Sort by

Same author

Missing Data Sensitivity Analyses for Alcohol Research.

Alcohol, clinical & experimental research·2026

Same author

A Self-Guided App-Based Mindfulness Intervention for Racially and Ethnically Minoritized Individuals Who Experience Discrimination-Related Mental Health Symptoms: Randomized Controlled Trial.

Journal of medical Internet research·2026

Same author

Factored structural equation modeling in blimp.

Psychological methods·2026

Same author

To Disaggregate or Not to Disaggregate: A Focus on Covariates in Multilevel Models.

Multivariate behavioral research·2026

Same author

Variations in alcohol craving and negative mood during a clinical trial of ibudilast for alcohol use disorder.

Alcohol, clinical & experimental research·2025

Same author

College students' socioeconomic background and sleep during the first year of college.

Sleep health·2025

Search research articles

Related Experiment Video

Updated: Jun 3, 2026

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

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

Published on: July 3, 2020

Missing not at random models for latent growth curve analyses.

Craig K Enders¹

¹Department of Psychology, Arizona State University,Tempe, AZ 85287–1104, USA. craig.enders@asu.edu

Psychological Methods

|March 9, 2011

Summary

This study introduces missing not at random (MNAR) models for longitudinal data, offering a weaker assumption than missing at random (MAR) models. These advanced statistical methods help reduce bias in substance use research and other fields.

More Related Videos

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

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Related Experiment Videos

Last Updated: Jun 3, 2026

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

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

Published on: July 3, 2020

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

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Area of Science:

Statistics
Biostatistics
Social Sciences

Background:

Traditional missing data techniques often assume data are missing at random (MAR).
MAR assumes missingness is related to observed variables, but this may not hold in longitudinal studies, especially with high attrition.
This can lead to biased parameter estimates in analyses.

Purpose of the Study:

To describe two classic missing not at random (MNAR) modeling approaches for longitudinal data: selection models and pattern mixture models.
To illustrate the application of these MNAR models using a real-world dataset.
To provide practical guidance for implementing and selecting appropriate longitudinal data models.

Main Methods:

Description of selection models and pattern mixture models for handling MNAR data.
Application of these models using structural equation modeling software (e.g., Mplus).
Illustration with a longitudinal substance use dataset.

Main Results:

MNAR models allow for a relationship between the outcome and the propensity for missing data, requiring weaker assumptions than MAR.
These models are now more accessible due to advancements in statistical software.
Despite advantages, MNAR analyses involve untestable assumptions and potential limitations.

Conclusions:

MNAR models offer a valuable alternative to MAR models for longitudinal data, particularly when attrition is related to the outcome.
The migration of these models to social sciences is facilitated by user-friendly software.
Researchers should be aware of the assumptions and limitations when applying MNAR analyses.