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...

Introduction To Survival Analysis

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...

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...

Analysis of Population Pharmacokinetic Data

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...

Statistical Methods for Analyzing Epidemiological Data

Statistical Methods for Analyzing Epidemiological Data

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:

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

You might also read

Related Articles

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

Sort by

Same author

LLMs for Healthcare Process Orchestration: Promises and Challenges.

Studies in health technology and informatics·2026

Same author

Challenges of Bringing Augmented Reality into Nursing Medication Preparation.

Studies in health technology and informatics·2026

Same author

Modeling Blood Pressure Monitoring in openEHR: An Implementation Case Study.

Studies in health technology and informatics·2026

Same author

Modeling Reimbursement-Relevant Drug Data as a Property Graph.

Studies in health technology and informatics·2026

Same author

LLMs in Problem-Based Learning: The Grounding Issue.

Studies in health technology and informatics·2026

Same author

Imaging Predictors of Silent Brain Lesions: Correlating Carotid Plaque Features on Ultrasound and CT in an Observational Study.

Journal of clinical medicine·2026

Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026

Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026

Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026

Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026

Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026

Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 17, 2025

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

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

Published on: December 9, 2015

Synthetic Data Generation Methods for Longitudinal and Time Series Health Data.

Marko Miletic¹, Murat Sariyar¹

¹Bern University of Applied Sciences, Switzerland.

Studies in Health Technology and Informatics

|July 1, 2025

Summary

This summary is machine-generated.

This review identifies 14 methods for synthetic health data generation, focusing on longitudinal and time series data. It guides future development and selection of synthetic data generation (SDG) models.

Keywords:

Synthetic Data Generation (SDG)generative models health data longitudinal data time series data

More Related Videos

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Related Experiment Videos

Last Updated: Sep 17, 2025

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

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

Published on: December 9, 2015

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

Health Informatics
Data Science
Biostatistics

Background:

Synthetic Data Generation (SDG) is recognized for structured health data.
Longitudinal and time series health data present unique generation challenges.
Effective SDG is vital for privacy-preserving health data analysis.

Purpose of the Study:

To conduct a rapid literature review on SDG methods for longitudinal and time series health data.
To identify and categorize prominent SDG techniques in this domain.
To provide preliminary insights into the utility, fidelity, and privacy of these methods.

Main Methods:

Systematic search of PubMed and swisscovery databases.
Analysis of 338 retrieved articles.
Categorization of 14 identified SDG methods.

Main Results:

Identified 14 prominent SDG methods for longitudinal and time series health data.
Methods include Generative Adversarial Networks (GANs), diffusion models, Variational Autoencoders (VAEs), transformer-based models, and Bayesian methods.
Preliminary insights into utility, fidelity, and privacy implications were gathered.

Conclusions:

The review provides a foundational understanding of current SDG methods for complex health data.
It serves as a guide for researchers in selecting appropriate SDG models.
Further research is needed to refine methods and address privacy concerns in synthetic health data generation.