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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

54
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
54
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

73
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,...
73
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

317
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...
317
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

23
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...
23
Longitudinal Studies01:26

Longitudinal Studies

103
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...
103
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

68
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
68

You might also read

Related Articles

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

Sort by
Same author

Development of novel plant protein with low gelation concentration from Torreya grandis nuts: Effect and mechanism of metal cations on its thermal gelation behavior.

Food chemistry·2025
Same author

Determination of the relationship between the numbers of Linepithema humile (Hymenoptera: Formicidae) on irrigation pipes and tree trunks to facilitate automated monitoring in Southern California citrus orchards.

Journal of economic entomology·2024
Same author

ESTIMATION FOR EXTREME CONDITIONAL QUANTILES OF FUNCTIONAL QUANTILE REGRESSION.

Statistica Sinica·2024
Same author

Extreme quantile estimation for partial functional linear regression models with heavy-tailed distributions.

The Canadian journal of statistics = Revue canadienne de statistique·2024
Same author

A simple method for the synthesis of copper nanoparticles from metastable intermediates.

RSC advances·2023
Same author

Asynchronous and error-prone longitudinal data analysis via functional calibration.

Biometrics·2023
Same journal

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026
Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026
Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026
Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: May 21, 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

6.2K

Semiparametric mixture regression for asynchronous longitudinal data using multivariate functional principal

Ruihan Lu1, Yehua Li2, Weixin Yao2

  • 1Office of Biostatistics, Food and Drug Administration, 10903 New Hampshire Avenue, Sliver Spring, MD 20993, United States.

Biostatistics (Oxford, England)
|March 22, 2025
PubMed
Summary
This summary is machine-generated.

This study identifies distinct subgroups of women during menopause using advanced statistical methods. Understanding these patterns in hormonal changes is key for women's long-term health.

Keywords:
EM algorithmfunctional datafunctional principal component analysismixture regressionsplinessubgroup analysis

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K

Related Experiment Videos

Last Updated: May 21, 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

6.2K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K

Area of Science:

  • Reproductive endocrinology and women's health research.
  • Statistical modeling and data analysis in longitudinal studies.
  • Biomarker analysis and functional data analysis.

Background:

  • Menopause involves hormonal fluctuations impacting women's long-term health.
  • The Study of Women's Health Across the Nation (SWAN) collects longitudinal data on women's health.
  • Hormonal biomarkers in SWAN are assessed asynchronously with other covariates, posing analytical challenges.

Purpose of the Study:

  • To analyze subgroup structures within the aging female population using SWAN data.
  • To explore how relationships between hormonal responses and covariates differ across subgroups.
  • To develop statistical methods for handling asynchronous, error-prone covariate data in longitudinal studies.

Main Methods:

  • Employed a semiparametric mixture regression model for subgroup analysis.
  • Modeled asynchronous, time-varying covariate trajectories as functional data using Karhunen-Loéve expansions and splines.
  • Utilized an Expectation-Maximization algorithm to fit a joint model for hormonal response and functional principal component scores, treating subgroup membership as missing data.
  • Applied data-driven methods to determine the optimal number of subgroups.

Main Results:

  • Identified a significant subgroup structure within the aging female population in the SWAN study.
  • Demonstrated the utility of functional data modeling and mixture regression for analyzing complex longitudinal health data.
  • Highlighted distinct patterns and distinctions among women undergoing menopause based on hormonal profiles and covariates.

Conclusions:

  • The analysis reveals important subgroup variations in women's health during menopause.
  • Advanced statistical approaches are effective in uncovering hidden structures in longitudinal health data.
  • Findings contribute to a better understanding of menopausal transitions and their impact on women's well-being.