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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

471
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...
471
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

303
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.
303
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

881
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...
881
Cancer Survival Analysis01:21

Cancer Survival Analysis

568
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
568
Survival Curves01:18

Survival Curves

542
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
542
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

624
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...
624

You might also read

Related Articles

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

Sort by
Same author

Tree-based exploratory identification of predictive biomarkers in non-randomized data.

BMC medical research methodology·2026
Same author

Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach.

Biometrical journal. Biometrische Zeitschrift·2026
Same author

Letermovir does not affect long-term polyclonal immune reconstitution after allogeneic hematopoietic stem cell transplantation with ATG-based GvHD prophylaxis.

Frontiers in immunology·2026
Same author

Dynamics of infection, vaccination and excess mortality during the COVID-19 pandemic among older individuals-a nationwide analysis.

European journal of epidemiology·2026
Same author

An extraction pipeline for analysis of hematopoietic stem cell transplantation data.

Bone marrow transplantation·2026
Same author

Percutaneous hepatic perfusion combined with ipilimumab and nivolumab for metastatic uveal melanoma (CHOPIN): a single-centre, open-label, randomised, phase 2 trial.

The Lancet. Oncology·2026
Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same journal

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026
Same journal

A robust neural network with random effects for subject-specific prediction of clustered count data.

Statistical methods in medical research·2026
Same journal

A comparison of methods for designing hybrid type 2 cluster-randomized trials with continuous effectiveness and implementation endpoints.

Statistical methods in medical research·2026
Same journal

Joint analysis of longitudinal and recurrent event data: A functional regression approach with autoregressive frailty.

Statistical methods in medical research·2026
See all related articles

Related Experiment Video

Updated: Dec 15, 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

10.7K

A global test for competing risks survival analysis.

Dominic Edelmann1, Maral Saadati1, Hein Putter2

  • 1Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany.

Statistical Methods in Medical Research
|July 8, 2020
PubMed
Summary
This summary is machine-generated.

A new global test for competing risks survival analysis offers improved performance over standard methods when few events per variable are present. This method enhances statistical power and type I error control in complex survival data.

Keywords:
Competing riskscause-specific hazardsglobal teststratified Cox modelsurvival

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

2.4K
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

562

Related Experiment Videos

Last Updated: Dec 15, 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

10.7K
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

2.4K
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

562

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Standard statistical tests for Cox models, like likelihood ratio and Wald tests, falter when the number of covariates exceeds observed events.
  • This limitation is critical in competing risks survival analysis, where event-specific occurrences are often scarce.
  • A gap exists in robust testing methodologies for competing risks scenarios with few events per variable.

Purpose of the Study:

  • To extend the global test for survival analysis to competing risks and multistate models.
  • To evaluate the performance of the extended global test against traditional methods in low-event-per-variable settings.

Main Methods:

  • Extension of the global test for survival analysis to accommodate competing risks and multistate models.
  • Conducting detailed simulation studies to assess type I error control and statistical power.
  • Application of the novel test to real-world cancer patient data from the European Society for Blood and Marrow Transplantation.

Main Results:

  • The novel global test demonstrates superior performance in both type I error control and statistical power compared to likelihood ratio and Wald tests.
  • These improvements are particularly evident in competing risks settings with a small number of events relative to covariates.
  • The utility of the global test is validated through real data examples in cancer patient cohorts.

Conclusions:

  • The extended global test provides a more reliable statistical approach for competing risks and multistate survival analysis, especially with limited events per variable.
  • This methodology offers enhanced power and accuracy, addressing a critical limitation in current survival analysis techniques.
  • The findings have significant implications for analyzing complex health data, such as cancer patient outcomes.