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

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...

Longitudinal Research

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...

Truncation in Survival Analysis

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.

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)...

Confounding in Epidemiological Studies

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...

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.

You might also read

Related Articles

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

Sort by

Same author

Impact of atrial fibrillation burden on quality of life and prediction of health care utilization: Primary results from the DEFINE AFib study.

Heart rhythm·2026

Same author

Operator learning curve with a novel dual-energy lattice-tip ablation system.

Heart rhythm·2025

Same author

Newborn readmissions and virtual primary care delivery: a population-based case-control study.

BMC primary care·2024

Same author

A novel performance scoring quantification framework for stress test set-ups.

PloS one·2023

Same author

On the use of historical estimates.

Statistical papers (Berlin, Germany)·2023

Same author

Testing for inequality constraints in singular models by trimming or winsorizing the variance matrix.

Journal of the American Statistical Association·2020

Search research articles

Related Experiment Video

Updated: Jun 10, 2026

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Constrained inference in mixed-effects models for longitudinal data with application to hearing loss.

Ori Davidov¹, Sophia Rosen

¹Department of Statistics, University of Haifa, Mount Carmel, Haifa, Israel. davidov@stat.haifa.ac.il

Biostatistics (Oxford, England)

|August 20, 2010

Summary

This study introduces constrained mixed-effects models for analyzing longitudinal medical data, improving parameter estimation and hypothesis testing accuracy in studies like hearing loss research.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 10, 2026

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

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

Area of Science:

Biostatistics
Longitudinal Data Analysis
Medical Statistics

Background:

Longitudinal data analysis is crucial in medical studies.
Mixed-effects models are commonly used for such data.
Model parameters may have inherent constraints, e.g., hearing deterioration over time.

Purpose of the Study:

To develop and evaluate methods for parameter estimation and hypothesis testing in mixed-effects models with constrained parameters.
To improve the accuracy and efficiency of statistical analyses for longitudinal medical data.

Main Methods:

Proposed maximum likelihood estimation using the expectation-conditional maximization (ECM) algorithm.
Developed constrained hypothesis testing procedures: likelihood ratio, Wald, and score tests.
Investigated empirical significance levels and power through simulations.

Main Results:

Constrained estimation procedures demonstrated improved mean squared error compared to unconstrained methods.
Hypothesis tests incorporating constraints showed enhanced power and maintained accurate significance levels.
Substantial improvements were observed in certain scenarios.

Conclusions:

Incorporating constraints into mixed-effects models enhances the precision of parameter estimates and the power of statistical tests.
The proposed methodology offers a valuable tool for analyzing longitudinal medical data with inherent parameter restrictions, as exemplified in hearing loss studies.