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

Censoring Survival Data01:09

Censoring Survival Data

172
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
172
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

243
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...
243
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

325
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...
325
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

171
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
171
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

323
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
323
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

452
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:
452

You might also read

Related Articles

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

Sort by
Same author

Use of a polygenic risk score to enhance early detection of coronary atherosclerosis.

American journal of preventive cardiology·2026
Same author

Effects of SGLT2 inhibition on incident heart failure in carriers of cardiomyopathy-associated genetic variants.

Nature medicine·2026
Same author

Direct oral anticoagulants vs warfarin in Asian vs non-Asian patients with atrial fibrillation: a patient-level meta-analysis from COMBINE AF.

European heart journal·2026
Same author

Body Mass Index, Clinical Outcomes, and Mortality in Heart Failure: A Mendelian Randomization Study.

Journal of the American College of Cardiology·2026
Same author

Bayesian Machine Learning Model Guiding Iterative, Personalized Anticoagulant Dosing Decision-Making: ENGAGE AF-TIMI 48 Trial Analysis.

JACC. Advances·2026
Same author

Systemic Embolic Events in Atrial Fibrillation: An Individual Patient Data Meta-analysis of 71 683 Participants Randomized to NOAC Versus Warfarin.

Circulation·2026

Related Experiment Video

Updated: Aug 9, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

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

Published on: January 8, 2020

14.6K

Privacy-aware multi-institutional time-to-event studies.

Julian Späth1, Julian Matschinske1, Frederick K Kamanu2

  • 1Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.

PLOS Digital Health
|February 22, 2023
PubMed
Summary

This study introduces privacy-preserving federated algorithms for clinical time-to-event analysis, enabling secure data collaboration. The developed tools, accessible via the Partea web app, yield results comparable to traditional methods, minimizing legal risks.

More Related Videos

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.4K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

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

Published on: July 27, 2018

8.3K

Related Experiment Videos

Last Updated: Aug 9, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

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

Published on: January 8, 2020

14.6K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.4K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

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

Published on: July 27, 2018

8.3K

Area of Science:

  • Biostatistics
  • Clinical Informatics
  • Privacy-Preserving Machine Learning

Background:

  • Clinical time-to-event studies require large datasets often unavailable at single institutions.
  • Sharing sensitive medical data centrally poses significant legal and privacy challenges.
  • Existing federated learning solutions for clinical data are complex and not readily applicable.

Purpose of the Study:

  • To develop and implement privacy-aware federated algorithms for key time-to-event analyses in clinical trials.
  • To offer a user-friendly alternative to centralized data collection, mitigating legal risks.
  • To demonstrate the feasibility and accuracy of federated approaches in clinical research.

Main Methods:

  • Hybrid approach combining federated learning, additive secret sharing, and differential privacy.
  • Implementation of survival curve, cumulative hazard rate, log-rank test, and Cox proportional hazards model.
  • Development of the web-application Partea for accessible, non-programmatic use.

Main Results:

  • Federated algorithms produced highly similar or identical results to centralized methods on benchmark datasets.
  • Previous clinical time-to-event study results were successfully reproduced in federated settings.
  • The Partea web app simplifies infrastructure and execution, reducing complexity for users.

Conclusions:

  • Privacy-aware federated algorithms offer a viable and accurate alternative for clinical time-to-event studies.
  • The Partea platform effectively lowers barriers to federated learning adoption in healthcare.
  • This approach minimizes legal risks and bureaucratic hurdles associated with sensitive data aggregation.