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

The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

568
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...
568
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

289
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...
289
Bonferroni Test01:10

Bonferroni Test

2.8K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
2.8K
Multiple Comparison Tests01:13

Multiple Comparison Tests

4.0K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.0K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

198
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.
198
Relative Risk01:12

Relative Risk

348
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
348

You might also read

Related Articles

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

Sort by
Same author

ChatGPT as a Tool for Biostatisticians: A Tutorial on Applications, Opportunities, and Limitations.

Statistics in medicine·2025
Same author

Wild bootstrap for counting process-based statistics: a martingale theory-based approach.

Lifetime data analysis·2025
Same author

Early and Late Buzzards: Comparing Different Approaches for Quantile-Based Multiple Testing in Heavy-Tailed Wildlife Research Data.

Biometrical journal. Biometrische Zeitschrift·2025
Same author

A nonparametric relative treatment effect for direct comparisons of censored paired survival outcomes.

Statistics in medicine·2024
Same author

RMST-based multiple contrast tests in general factorial designs.

Statistics in medicine·2024
Same author

Factorial survival analysis for treatment effects under dependent censoring.

Statistical methods in medical research·2023
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Sep 13, 2025

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

2.2K

Multiple tests for restricted mean time lost with competing risks data.

Merle Munko1, Dennis Dobler2,3, Marc Ditzhaus1

  • 1Department of Mathematics, Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany.

Biometrics
|July 30, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces new statistical tests for comparing restricted mean time lost (RMTL) in complex survival analyses. These methods handle multiple event types and data ties, improving upon existing 2-sample tests.

Keywords:
competing risksfactorial designmultiple testingpermutationrestricted mean time lostsurvival analysis

More Related Videos

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

10.3K
Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats
08:06

Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats

Published on: June 18, 2018

7.3K

Related Experiment Videos

Last Updated: Sep 13, 2025

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

2.2K
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

10.3K
Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats
08:06

Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats

Published on: June 18, 2018

7.3K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Competing Risks

Background:

  • Restricted mean time lost (RMTL) is a valuable estimand in competing risks survival analysis.
  • Existing statistical tests for RMTL are limited to simple comparisons and a small number of event types.
  • Continuity assumptions in current methods restrict their applicability to real-world data with ties.

Purpose of the Study:

  • To develop general statistical tests for comparing RMTL in factorial designs with an arbitrary number of event types.
  • To address limitations of existing RMTL tests by accommodating data ties and improving small sample performance.
  • To introduce multiple testing procedures for simultaneous RMTL comparisons with enhanced statistical power.

Main Methods:

  • Development of Wald-type test statistics for RMTL comparisons.
  • Implementation of a permutation approach to enhance reliability and small sample performance.
  • Incorporation of the asymptotic dependence structure for powerful multiple testing.

Main Results:

  • The proposed methods provide flexible and robust RMTL comparisons for complex designs.
  • The permutation-based tests demonstrate improved small sample performance.
  • Multiple testing procedures effectively control Type I error rates while increasing power.

Conclusions:

  • The developed statistical tests offer a significant advancement for RMTL analysis in competing risks settings.
  • These methods are applicable to practical scenarios, including those with data ties.
  • The study provides a powerful framework for analyzing complex survival data, as illustrated in a leukemia patient example.