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

Censoring Survival Data

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...

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,...

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.

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...

Truncation in Survival Analysis

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...

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...

You might also read

Related Articles

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

Sort by

Same author

Justifying and reporting the use of severe stress in experiments that use animals.

British journal of pharmacology·2026

Same author

Be CLEAR to ensure methodological and data transparency.

Trends in pharmacological sciences·2025

Same author

Restricted mean survival time based on Wu-Kolassa estimator compared to Kaplan-Meier estimator.

Contemporary clinical trials·2025

Same author

Design of paediatric trials with benefit-risk endpoints using a composite score of adverse events of interest (AEI) and win-statistics.

Journal of biopharmaceutical statistics·2022

Same author

Sample size re-estimation in Phase 2 dose-finding: Conditional power versus Bayesian predictive power.

Pharmaceutical statistics·2022

Same author

Win statistics (win ratio, win odds, and net benefit) can complement one another to show the strength of the treatment effect on time-to-event outcomes.

Pharmaceutical statistics·2022

Search research articles

Related Experiment Video

Updated: Jun 13, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Interval-specific censoring set adjusted Kaplan-Meier estimator.

Yaoshi Wu¹, John Kolassa²

¹Department of Statistics, UCONN, Storrs, CT, USA.

Journal of Applied Statistics

|September 13, 2024

Summary

This study introduces a new method to improve survival analysis by reducing overestimation in the Kaplan-Meier (KM) estimator, especially with high censoring rates. The adjusted KM estimator offers more accurate survival estimates in these common scenarios.

Keywords:

Censoring set Kaplan-Meier estimator independent event and censoring times modified log-rank test overestimation

More Related Videos

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

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Related Experiment Videos

Last Updated: Jun 13, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

Biostatistics
Survival Analysis
Medical Statistics

Background:

The Kaplan-Meier (KM) estimator is a standard method for survival analysis.
The KM estimator can overestimate survival probabilities when censoring occurs.
Accurate survival estimation is crucial for clinical research and patient outcomes.

Purpose of the Study:

To develop a non-parametric approach to reduce overestimation in the KM estimator.
To adjust the KM estimator using interval-specific censoring information.
To provide a consistent and theoretically sound alternative to the standard KM estimator.

Main Methods:

Developed an interval-specific censoring set adjusted KM estimator.
Provided theoretical proofs for estimator consistency and bias reduction.
Derived a variance estimation formula based on Greenwood's approach.
Proposed a modified log-rank test.

Main Results:

The proposed estimator significantly reduces overestimation compared to the standard KM estimator, particularly with high censoring rates.
Simulation studies demonstrated considerable bias reduction in median survival time and survival rates.
Standard deviations between the proposed and KM estimators were comparable.
Application to Nonalcoholic Fatty Liver Disease patient data confirmed substantial overestimation reduction.

Conclusions:

The interval-specific censoring set adjusted KM estimator provides a more accurate survival probability estimation.
This method is particularly beneficial in studies with substantial censoring.
The findings have implications for improving the reliability of survival data analysis in various medical fields.