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

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 Cox...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.
Censoring Survival Data01:09

Censoring Survival Data

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

The Mantel-Cox Log-Rank Test

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 interest.
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...

You might also read

Related Articles

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

Sort by
Same author

Superb microvascular imaging is as sensitive as contrast-enhanced ultrasound for detecting synovial vascularity in rheumatoid arthritis.

Quantitative imaging in medicine and surgery·2022
Same author

Self-frequency-conversion nanowire lasers.

Light, science & applications·2022
Same author

The Effect of CD226 on the Balance between Inflammatory Monocytes and Small Peritoneal Macrophages in Mouse Ulcerative Colitis.

Immunological investigations·2022
Same author

MicroRNA-Messenger RNA Regulatory Network Mediates Disrupted TH17 Cell Differentiation in Depression.

Frontiers in psychiatry·2022
Same author

Tribocatalytic degradation of dyes by tungsten bronze ferroelectric Ba<sub>2.5</sub>Sr<sub>2.5</sub>Nb<sub>8</sub>Ta<sub>2</sub>O<sub>30</sub> submicron particles.

RSC advances·2022
Same author

NIR-I-Responsive Single-Band Upconversion Emission through Energy Migration in Core-Shell-Shell Nanostructures.

Angewandte Chemie (International ed. in English)·2022

Related Experiment Video

Updated: May 19, 2026

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

A sequential testing approach to detecting multiple change points in the proportional hazards model.

Pei He1, Liang Fang, Zheng Su

  • 1Stanford University, Stanford, CA, USA.

Statistics in Medicine
|August 31, 2012
PubMed
Summary

This study introduces a new method for analyzing clinical trial data. It accurately detects changes in treatment effects over time without needing to know the number of changes beforehand.

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

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Related Experiment Videos

Last Updated: May 19, 2026

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

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

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Survival Analysis

Background:

  • The proportional hazards model is standard for time-to-event data in clinical trials.
  • A key assumption of constant hazard ratio over time is often violated due to treatment lag effects.
  • Existing methods for detecting change points require prior knowledge of their quantity.

Purpose of the Study:

  • To develop a novel sequential testing approach for identifying multiple change points in the hazard ratio function.
  • To create a method where the number of change points is data-driven rather than pre-specified.
  • To improve the accuracy of modeling time-to-event outcomes in clinical research.

Main Methods:

  • Utilized a sequential testing strategy based on likelihood ratio tests.
  • Employed resampling techniques to evaluate the null distributions of likelihood ratio statistics.
  • Developed a data-adaptive approach to infer the number of change points.

Main Results:

  • The proposed sequential testing method accurately detects change points in the hazard ratio function.
  • Simulations using clinical trial data demonstrated the approach's effectiveness.
  • Validation on a real-world time-to-event study confirmed its practical utility.

Conclusions:

  • The novel sequential testing approach offers a robust solution for analyzing time-to-event data with time-varying treatment effects.
  • This method overcomes limitations of existing techniques by not requiring pre-specification of the number of change points.
  • The findings have significant implications for the accurate interpretation of clinical trial results and treatment effect evaluation.