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

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

85
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.
85
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

339
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...
339
Censoring Survival Data01:09

Censoring Survival Data

56
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...
56
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

169
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...
169
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

131
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...
131
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

288
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
288

You might also read

Related Articles

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

Sort by
Same author

Decoding the association between microorganisms and autoimmunity: a multifaceted review of evidence on specific pathogens that trigger autoimmune diseases.

Clinical and experimental medicine·2026
Same author

Lung cancer multimodal auxiliary diagnosis based on entropy weight decision fusion.

Biomedical engineering online·2026
Same author

From Exploration to Production: A Systematic Review of Natural Gas Hydrate Technologies.

ACS omega·2026
Same author

Preliminary study on symptoms and signs of patients with laryngopharyngeal reflux disease infected with <i>Helicobacter pylori</i>.

Experimental and therapeutic medicine·2026
Same author

DUSP26: Unveiling a critical molecular mediator and therapeutic target in developmental dysplasia of the hip‑associated secondary osteoarthritis.

International journal of molecular medicine·2026
Same author

The impact of type 1 diabetes mellitus on intellectual status: A Mendelian randomization study.

Journal of education and health promotion·2026
Same journal

Targeted maximum likelihood estimation (TMLE) in regulatory submissions and research: a landscape analysis.

The international journal of biostatistics·2026
Same journal

Predicting birth weight by multivariate functional principal component regressions.

The international journal of biostatistics·2026
Same journal

Robust median regression for count data with general lower truncation using a contaminated discrete Weibull model.

The international journal of biostatistics·2026
Same journal

Handling the uncertainty issue of missingness via a mixture-structure-based method.

The international journal of biostatistics·2026
Same journal

Statistical method for pooling categorical biomarker data from multi-center matched/nested case-control studies.

The international journal of biostatistics·2026
Same journal

Prognostic score methods for the estimation of the average causal effect.

The international journal of biostatistics·2026
See all related articles

Related Experiment Video

Updated: May 29, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K

DsubCox: a fast subsampling algorithm for Cox model with distributed and massive survival data.

Haixiang Zhang1, Yang Li2, HaiYing Wang3

  • 1Center for Applied Mathematics and KL-AAGDM, 12605 Tianjin University , Tianjin 300072, China.

The International Journal of Biostatistics
|February 3, 2025
PubMed
Summary
This summary is machine-generated.

We developed a fast subsampling method for Cox models using massive survival data. This approach protects privacy and reduces computation by transmitting only summary statistics, enabling efficient analysis of large, decentralized datasets.

Keywords:
L-optimality criteriondistributed learningmassive survival dataoptimal subsampling

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

204

Related Experiment Videos

Last Updated: May 29, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

204

Area of Science:

  • Biostatistics
  • Computational Statistics
  • Survival Analysis

Background:

  • Massive survival datasets from multi-centered, decentralized sources present significant privacy and computational challenges.
  • Existing methods may struggle with the scale and distributed nature of modern survival data.
  • Efficient and privacy-preserving statistical methods are crucial for analyzing large-scale health and research data.

Purpose of the Study:

  • To propose a fast subsampling procedure for the Cox model tailored for massive, decentralized survival datasets.
  • To develop an estimator that ensures privacy protection and alleviates computational burden.
  • To enable efficient data analysis through the transmission of summary-level statistics.

Main Methods:

  • A novel subsampling procedure for the Cox model was developed.
  • Optimal subsampling probabilities were derived to guide the selection of data subsets.
  • Asymptotic properties of the proposed estimator were rigorously established for robust inference.
  • The method was validated using extensive simulation studies and applied to a real-world dataset.

Main Results:

  • The proposed subsampling estimator effectively handles massive, decentralized survival data.
  • The method allows for privacy protection by transmitting only summary statistics.
  • Only one round of communication is required for transmitting subsample-based summary statistics.
  • Simulation studies confirmed the effectiveness and efficiency of the proposed approach.

Conclusions:

  • The fast subsampling procedure offers an effective solution for analyzing large-scale, decentralized survival data.
  • This methodology significantly reduces computational burden and enhances privacy protection.
  • The approach is suitable for real-world applications, as demonstrated by its use in analyzing U.S. airlines data.