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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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)...
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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...
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...
Longitudinal Studies01:26

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

"An opportunity to spend time with each other": The importance of mealtimes for carepartners of older adults with acute myeloid leukemia and myelodysplastic syndromes.

Journal of geriatric oncology·2026
Same author

Implementing mental health interventions in perinatal home visiting programs: a systematic review of the literature.

Archives of women's mental health·2026
Same author

Acceptability and Feasibility of a Problem-Solving Intervention for Maternal Caregivers of Young Adult Survivors of Childhood Brain Tumors.

Psycho-oncology·2026
Same author

Differences in gait biomechanics in the first year after anterior cruciate ligament reconstruction: A systematic review.

Clinical biomechanics (Bristol, Avon)·2025
Same author

Patterns of Shared Variation in Knee Ultrasound for Osteoarthritis: A Machine Learning Approach.

Osteoarthritis imaging·2025
Same author

Physical Function and Mobility in Older Adults Receiving Treatment for Acute Myeloid Leukemia: A Longitudinal Qualitative Study.

Oncology nursing forum·2025
Same journal

Missed Nursing Care in Neonatal Intensive Care Units for Infants Experiencing or at Risk of Experiencing Substance Withdrawal.

Research in nursing & health·2026
Same journal

The Impact of Stress, Trauma, and Violence on Well-Being and Physical Health.

Research in nursing & health·2026
Same journal

A Comparison of Post-Traumatic Stress and Depressive Symptoms by Suicidal Ideation Among Black Transgender Women.

Research in nursing & health·2026
Same journal

Exploring Prolonged Grief Experiences of Ethnoracial Minoritized Caregivers: An Emic Perspective.

Research in nursing & health·2026
Same journal

The Psychometric Properties of the Caregiver Feeding Style Questionnaire: A Cross-Cultural Validation in Spanish Parents.

Research in nursing & health·2026
Same journal

Feasibility of an Online Resilience Program for Mothers With Adverse Childhood Experiences.

Research in nursing & health·2026
See all related articles

Related Experiment Video

Updated: May 19, 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

A strategy for selecting among alternative models for continuous longitudinal data.

George J Knafl1, Linda Beeber, Todd A Schwartz

  • 1School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

Research in Nursing & Health
|August 23, 2012
PubMed
Summary
This summary is machine-generated.

Linear mixed models (LMMs) analyze longitudinal data by comparing means. Choosing the right covariance structure is crucial, as results vary. Model selection strategies help identify the best structure for accurate analysis.

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Related Experiment Videos

Last Updated: May 19, 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

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Statistical Modeling

Background:

  • Linear mixed models (LMMs) are widely used for analyzing continuous longitudinal data.
  • Fixed effects tests in LMMs compare means to address research questions.
  • The choice of covariance structure significantly impacts LMM results.

Purpose of the Study:

  • To describe alternative LMMs for continuous longitudinal data.
  • To present a strategy for selecting an appropriate covariance structure using model selection criteria.
  • To demonstrate the application of this strategy with an exemplar dataset.

Main Methods:

  • Exploration of various LMMs for longitudinal data.
  • Application of model selection criteria (e.g., AIC, BIC) for covariance structure identification.
  • Analysis of an exemplar dataset with diverse models for means, variances, and correlations.

Main Results:

  • Model selection criteria can effectively guide the choice of covariance structure in LMMs.
  • Different covariance structures yield varying fixed effects results.
  • The proposed strategy facilitates the identification of a well-fitting LMM.

Conclusions:

  • A systematic approach using model selection is essential for robust LMM analysis.
  • Proper covariance structure selection enhances the reliability of findings from longitudinal studies.
  • The described strategy provides a practical framework for researchers analyzing longitudinal data.