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: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

573
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...
573
Longitudinal Research02:20

Longitudinal Research

13.5K
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...
13.5K
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

279
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
279
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

250
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...
250
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

278
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
278
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

547
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
547

You might also read

Related Articles

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

Sort by
Same author

B-Spline Modeling of Inertial Measurements for Evaluating Stroke Rehabilitation Effectiveness.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2023
Same author

On summary ROC curve for dichotomous diagnostic studies: an application to meta-analysis of COVID-19.

Journal of applied statistics·2023
Same author

A model selection criterion for clustered survival analysis with informative cluster size.

Pharmaceutical statistics·2022
Same author

Distribution-free model selection for longitudinal zero-inflated count data with missing responses and covariates.

Statistics in medicine·2022
Same author

Model selection for semiparametric marginal mean regression accounting for within-cluster subsampling variability and informative cluster size.

Biometrics·2018
Same author

Joint model selection of marginal mean regression and correlation structure for longitudinal data with missing outcome and covariates.

Biometrical journal. Biometrische Zeitschrift·2017
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Feb 10, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.4K

Model selection based on resampling approaches for cluster longitudinal data with missingness in outcomes.

Chun-Shu Chen1, Chung-Wei Shen2

  • 1Institute of Statistics and Information Science, National Changhua University of Education, Changhua, Taiwan, R.O.C.

Statistics in Medicine
|May 9, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel model selection method for cluster longitudinal data with missing values. The approach uses data perturbation and bootstrapping to identify the best model based on expected weighted quadratic loss.

Keywords:
bootstrapdata perturbationgeneralized estimating equationsvariable selectionweighted quadratic loss

More Related Videos

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

549
Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

9.0K

Related Experiment Videos

Last Updated: Feb 10, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.4K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

549
Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

9.0K

Area of Science:

  • Biostatistics
  • Health Sciences
  • Data Analysis

Background:

  • Longitudinal and cluster longitudinal data are common in health studies, with repeated observations over time.
  • Complex correlation structures and missing data pose challenges for model selection in these datasets.
  • Existing model selection methods are underexplored for cluster longitudinal data, especially with missingness.

Purpose of the Study:

  • To develop and validate a robust model selection technique for cluster longitudinal data.
  • To address the specific challenges posed by missing data within these complex structures.
  • To identify the optimal statistical model that best represents the underlying data patterns.

Main Methods:

  • Utilized expected weighted quadratic loss as a criterion for model evaluation.
  • Employed data perturbation and bootstrapping techniques to estimate the expected loss.
  • Selected the model with the minimum estimated expected loss.

Main Results:

  • The proposed method effectively performs model selection for cluster longitudinal data.
  • Numerical assessments demonstrated the method's validity and performance.
  • A real-world application using asthma data illustrated the practical utility of the approach.

Conclusions:

  • The developed method provides a reliable approach for model selection in cluster longitudinal data with missing values.
  • This technique enhances the analysis of complex health-related datasets.
  • The findings offer a valuable tool for researchers dealing with longitudinal health data.