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 Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

161
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...
161
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

857
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...
857
Longitudinal Studies01:26

Longitudinal Studies

354
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...
354
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

750
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
750
Multiple Regression01:25

Multiple Regression

3.5K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.5K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

382
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
382

You might also read

Related Articles

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

Sort by
Same author

Inflammatory alterations mediate tau-associated neurodegeneration.

Brain communications·2026
Same author

Cortex-anchored sensor-space harmonics for event-related EEG.

Journal of neural engineering·2026
Same author

Identification of Heterogeneous Cortical Thickness Patterns Associated with Prenatal Gestational Diabetes Exposure: A SuStaIn-Based Subtyping Study.

bioRxiv : the preprint server for biology·2026
Same author

Regional reconfiguration of functional brain networks during childhood and adolescence: evaluating age and sex effect.

bioRxiv : the preprint server for biology·2026
Same author

A Bayesian likely responder approach for the analysis of randomized controlled trials.

Statistical methods in medical research·2026
Same author

Polygenic Index for Sleep Duration and Brain Changes over Time.

Medical sciences (Basel, Switzerland)·2026

Related Experiment Video

Updated: Nov 30, 2025

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

6.6K

Logistic regression error-in-covariate models for longitudinal high-dimensional covariates.

Hyung Park1, Seonjoo Lee2

  • 1Division of Biostatistics, Department of Population Health, New York University, New York, NY 10016.

Stat
|November 12, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method for analyzing complex health data, improving biomarker discovery for dementia transition by handling errors in covariates and high-dimensional random effects.

Keywords:
conditional-score equationserrors in covariateslongitudinal functional principal component analysis

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.6K
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

3.6K

Related Experiment Videos

Last Updated: Nov 30, 2025

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

6.6K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.6K
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

3.6K

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Biomarker Discovery

Background:

  • Analyzing binary outcomes with subject-specific random effects from longitudinal data presents challenges due to high dimensionality and correlated covariates.
  • Estimating these random effects introduces errors in covariates, complicating standard logistic regression models.

Purpose of the Study:

  • To develop a robust statistical method for logistic regression models with high-dimensional, correlated random effects and covariate errors.
  • To identify reliable biomarkers associated with dementia transition using longitudinal neuroimaging data.

Main Methods:

  • Longitudinal principal component analysis (LPCA) was used to reduce the dimensionality of random effects.
  • An extended conditional-score equation approach was adapted to handle errors in covariates.
  • Majorization and smoothly clipped absolute deviation (SCAD) penalized estimation were employed for robust inference in moderate/high dimensions.

Main Results:

  • Simulation studies demonstrated the method's reliability in handling covariate errors and high-dimensional random effects.
  • The approach was successfully applied to longitudinal cortical thickness data from 68 regions of interest.
  • Potential biomarkers related to dementia transition were identified.

Conclusions:

  • The proposed method effectively addresses challenges in logistic regression with complex random effects and covariate errors.
  • This statistical framework enhances the identification of biomarkers for neurodegenerative disease transitions.
  • The findings support the use of advanced statistical modeling for analyzing longitudinal health data.