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

177
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...
177
Hazard Ratio01:12

Hazard Ratio

111
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
111
Hazard Rate01:11

Hazard Rate

102
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
102
Survival Curves01:18

Survival Curves

131
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...
131
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

215
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...
215
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

347
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...
347

You might also read

Related Articles

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

Sort by
Same author

Shared Strides: Community-based, high-throughput biomechanics data collection in knee osteoarthritis.

medRxiv : the preprint server for health sciences·2026
Same author

Integrating Multiple Clustering Techniques and Performance Measures via Ranking for scRNA-Seq Data.

Statistics in medicine·2025
Same author

Growth Differentiation Factor-15 is Associated With Acute Myocardial Infarction and Death at 30 and 90 Days in Emergency Department Patients With Suspected Acute Coronary Syndrome.

Journal of the American Heart Association·2025
Same author

Can the Unit Size Predict Outcomes? Testing for Informativeness in Three-Level Designs.

Statistics in medicine·2025
Same author

Generalized single index modeling of longitudinal data with multiple binary responses.

Statistics in medicine·2024
Same author

Testing for marginal covariate effect when the subgroup size induced by the covariate is informative.

Statistical methods in medical research·2024

Related Experiment Video

Updated: Jun 23, 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

Comparing two hazard curves when there is a treatment time-lag effect.

Xiaoxi Zhang1, Somnath Datta1, Peihua Qiu1

  • 1Department of Biostatistics, University of Florida, Gainesville, Florida.

Statistics in Medicine
|June 17, 2024
PubMed
Summary

This study introduces a new weighted log-rank test to effectively compare survival data when treatments have a delayed effect. The method improves detection of treatment benefits obscured by initial similarities in hazard curves.

Keywords:
box‐cox transformationhazard curvesurvival datatime‐lagtreatment effectweighted log‐rank test

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

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

Published on: December 9, 2015

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

2.0K

Related Experiment Videos

Last Updated: Jun 23, 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
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

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

Published on: December 9, 2015

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

2.0K

Area of Science:

  • Biostatistics
  • Medical Research Methodology
  • Survival Analysis

Background:

  • Time-to-event data are crucial in medical studies, particularly for comparing interventions.
  • Traditional methods like the log-rank test can fail when treatments exhibit a time-lag effect, leading to similar initial hazard curves.
  • This similarity can mask true treatment differences, reducing the statistical power to detect therapeutic benefits.

Purpose of the Study:

  • To develop and evaluate a novel statistical method for comparing hazard curves in the presence of treatment time-lag effects.
  • To enhance the sensitivity of survival data analysis when treatment efficacy is not immediate.
  • To provide a more effective alternative to existing methods for detecting treatment effects in time-lag scenarios.

Main Methods:

  • A weighted log-rank test incorporating a flexible weighting scheme was developed.
  • The proposed method was compared against established statistical procedures.
  • Simulations and case studies were used to assess performance under various time-lag conditions.

Main Results:

  • The new weighted log-rank test demonstrated superior effectiveness in detecting treatment effects compared to traditional methods when a time-lag was present.
  • The flexible weighting scheme allowed the method to adapt to different patterns of treatment time-lag.
  • The enhanced sensitivity was observed across various simulated scenarios.

Conclusions:

  • The proposed weighted log-rank test offers a more powerful approach for analyzing time-to-event data with potential treatment time-lag effects.
  • This method can improve the accurate assessment of medical interventions where treatment benefits manifest over time.
  • It provides a valuable tool for biostatisticians and researchers in oncology and other fields utilizing survival analysis.