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 reasons...

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.

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...

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a survival tree begins...

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...

You might also read

Related Articles

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

Sort by

Same author

Function-based hypothesis testing in censored two-sample location-scale models.

Lifetime data analysis·2018

Same author

Semiparametric simultaneous confidence bands for the difference of survival functions.

Lifetime data analysis·2015

Same author

Simultaneous confidence bands for Cox regression from semiparametric random censorship.

Lifetime data analysis·2015

Same author

The Missing Censoring Indicator Model and the Smoothed Bootstrap.

Computational statistics & data analysis·2009

Same author

Censored Median Regression and Profile Empirical Likelihood.

Statistical methodology·2008

Same journal

Symmetric Directional False Discovery Rate Control.

Statistical methodology·2017

Same journal

Latent Class Analysis of Incomplete Data via an Entropy-Based Criterion.

Statistical methodology·2016

Same journal

Incorporating auxiliary information for improved prediction using combination of kernel machines.

Statistical methodology·2014

Same journal

Efficient network meta-analysis: a confidence distribution approach.

Statistical methodology·2014

Same journal

A default prior distribution for contingency tables with dependent factor levels.

Statistical methodology·2014

Same journal

A note about the identifiability of causal effect estimates in randomized trials with non-compliance.

Statistical methodology·2014

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 28, 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

Survival analysis for the missing censoring indicator model using kernel density estimation techniques.

Sundarraman Subramanian¹

¹Department of Mathematics and Statistics, University of Maine, United States.

Statistical Methodology

|October 28, 2008

Summary

This summary is machine-generated.

This study introduces a new survival function estimator for missing censoring data. The novel inverse probability-of-non-missingness weighted estimator demonstrates asymptotic efficiency and strong consistency for survival analysis.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 28, 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

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

Area of Science:

Statistics
Survival Analysis
Biostatistics

Background:

Missing data in censoring indicators poses challenges in survival analysis.
Existing estimators may not fully address the complexities of random censorship with missing indicators.

Purpose of the Study:

To develop and analyze a new estimator for the survival function in the presence of missing censoring indicators.
To derive asymptotic properties, including large sample results and consistency, for a novel cumulative hazard function estimator.

Main Methods:

Utilized an inverse probability-of-non-missingness weighted approach.
Employed kernel estimation for the conditional probability of non-missingness.
Derived almost sure representations, uniform strong consistency, bias, variance, and mean squared error.

Main Results:

Established large sample results for the inverse probability-of-non-missingness weighted cumulative hazard function estimator.
Demonstrated uniform strong consistency with a rate of convergence for the proposed estimator.
The derived survival function estimator is asymptotically efficient.

Conclusions:

The novel estimator provides a robust method for survival function estimation with missing censoring indicators.
The derived asymptotic properties support its theoretical validity and efficiency.
Numerical studies confirm its competitive performance against existing methods.