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

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...
Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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...
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...
Truncation in Survival Analysis01:09

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.

You might also read

Related Articles

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

Sort by
Same author

Correction: Reduced peripheral natural killer cell counts may be a predictive marker for cesarean scar pregnancy.

BMC immunology·2026
Same author

Bridging ecological processes to elevated antibiotic resistance risk in tomato microbiome under fungicide stress.

The ISME journal·2026
Same author

Theoretical Insights Into the Effect of π-Conjugation Extension on Photophysical Properties and ESIPT Behavior of a Novel Benzimidazole-Based Fluorophore.

Journal of fluorescence·2026
Same author

Endothelial KLF4 depletion drives age-related neurovascular dysfunction and neuropsychiatric impairment.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Reduced peripheral natural killer cell counts may be a predictive marker for cesarean scar pregnancy.

BMC immunology·2026
Same author

iDT-diet: Toward Personalized Health Forecasting-An Intelligent Digital Twin Model for Diet-Influenced Biomarker Trajectories (Student Abstract).

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence·2026

Related Experiment Video

Updated: Jun 24, 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

Multiple- vs Non- or Single-Imputation based Fuzzy Clustering for Incomplete Longitudinal Behavioral Intervention

Zhaoyang Zhang1, Hua Fang1

  • 1Division of Biostatistics and Health Services Research, Department of Quantitative Health Science, University of Massachusetts Medical School, Worcester, MA 01655.

...Ieee...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies
|October 17, 2017
PubMed
Summary

Multiple imputation (MI) enhances clustering accuracy for longitudinal behavioral data with missing values. This method improves understanding treatment effects by reducing uncertainty in patient behavioral variations.

Keywords:
Fuzzy clusteringMIFuzzyMissing valuesMultiple imputationlongitudinal data

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: Jun 24, 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

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:

  • Behavioral Science
  • Statistical Modeling
  • Data Science

Background:

  • Understanding individual patient behavioral variations is key to assessing intervention effectiveness.
  • Longitudinal studies of behavioral interventions frequently encounter missing data.
  • Clustering and unsupervised learning are vital for analyzing treatment effect heterogeneity.

Purpose of the Study:

  • To evaluate the effectiveness of multiple imputation (MI) in fuzzy clustering for longitudinal behavioral data.
  • To demonstrate how MI-based clustering reduces uncertainty compared to other imputation methods.
  • To advance the application of MI in analyzing incomplete behavioral intervention data.

Main Methods:

  • Utilized a multiple imputation (MI) fuzzy clustering approach.
  • Conducted theoretical, empirical, and numerical analyses.
  • Compared MI-based clustering with non-imputation and single-imputation methods.

Main Results:

  • The MI-based fuzzy clustering approach significantly reduces uncertainty in clustering accuracy.
  • Demonstrated superior performance over non-imputation and single-imputation clustering.
  • Provided a robust method for handling missing data in behavioral intervention studies.

Conclusions:

  • Multiple imputation-based fuzzy clustering is a powerful tool for analyzing incomplete longitudinal behavioral data.
  • This approach enhances the understanding of treatment effect heterogeneity.
  • The study highlights the utility and strength of MI for complex behavioral research.