Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Comparing the Survival Analysis of Two or More Groups

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...

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

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...

Assumptions of Survival Analysis

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.

Kaplan-Meier Approach

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,...

Introduction To Survival Analysis

Introduction To Survival Analysis

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 until a...

The Mantel-Cox Log-Rank Test

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.

You might also read

Related Articles

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

Sort by

Same author

Mapping Brain Metabolites in Tuberous Sclerosis Complex: A 3T MR Spectroscopic Imaging Study.

AJNR. American journal of neuroradiology·2026

Same author

The Human Shield Hypothesis: Does Predator Avoidance of Humans Create Refuges for Prey?

Ecology letters·2025

Same author

Clinical and pathological spectrum of disease severity among patients with acute liver failure (ALF) undergoing deceased donor liver transplantation.

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society·2025

Same author

Antimullerian Hormone, a Marker of Ovarian Reserve, Is Protective Against Presence and Severity of NASH in Premenopausal Women.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association·2023

Same author

Understanding toxicity associated with boron nitride nanotubes: Review of toxicity studies, exposure assessment at manufacturing facilities, and read-across.

Journal of materials research·2023

Same author

Resected Tumor Outcome and Recurrence (RESTORE) Index for Hepatocellular Carcinoma Recurrence after Resection.

Cancers·2023

Search research articles

Related Experiment Video

Updated: Jul 13, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Model selection in logistic joinpoint regression with applications to analyzing cohort mortality patterns.

Michal Czajkowski¹, Ryan Gill, Grzegorz Rempala

¹Department of Mathematics, University of Louisville, Louisville, KY 40292, USA.

Statistics in Medicine

|August 7, 2007

Summary

This study introduces a novel logistic joinpoint regression model for anomaly detection in longitudinal mortality data. The method effectively identifies changes in cancer mortality trends within a chemical worker cohort.

Related Experiment Videos

Last Updated: Jul 13, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

Epidemiology
Biostatistics
Statistical modeling

Background:

Longitudinal cohort studies are crucial for understanding disease progression and mortality patterns over time.
Anomaly detection in mortality data can reveal significant shifts in risk factors or environmental exposures.
Joinpoint regression is a statistical method used to detect significant changes in trends.

Purpose of the Study:

To develop and evaluate a general model for anomaly detection in longitudinal cohort mortality patterns.
To propose sequential procedures for identifying the number and location of joinpoints in mortality trends.
To apply the developed model to analyze cancer mortality data in a cohort of chemical workers.

Main Methods:

The study employs logistic joinpoint regression to model mortality patterns.
Backward and forward sequential procedures are utilized for joinpoint selection.
Model parameters are estimated, and selection algorithms are demonstrated using real-world data.

Main Results:

The proposed logistic joinpoint regression model effectively detects anomalies in longitudinal mortality data.
Sequential procedures successfully identify the number and location of significant trend changes.
The analysis of chemical worker mortality data provides insights into cancer risk patterns.

Conclusions:

The developed joinpoint regression model offers a robust approach for anomaly detection in cohort mortality studies.
The sequential selection algorithms are efficient for identifying critical changes in epidemiological trends.
This methodology can be valuable for public health surveillance and occupational epidemiology.