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

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

757
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,...
757
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
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...
1.3K
Survival Curves01:18

Survival Curves

924
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...
924
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

987
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...
987
Censoring Survival Data01:09

Censoring Survival Data

668
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...
668
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

493
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.
493

You might also read

Related Articles

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

Sort by
Same author

Blood Pressure Control in Adolescents With CKD and Risk of Kidney Failure in Young Adulthood.

Kidney medicine·2026
Same author

Length of Follow-Up Time Needed for Stable eGFR Slope Estimation in Glomerular Disease.

Kidney360·2026
Same author

Emulating a target trial of surgical removal of uterine fibroids on atherosclerotic cardiovascular disease.

Fertility and sterility·2026
Same author

Idiotypic-susceptible Alzheimer's disease: a clinically relevant, neurofibrillary tangle subtype.

Acta neuropathologica·2026
Same author

Erratum: Novel R Shiny Tool for Survival Analysis With Time-Varying Covariate in Oncology Studies: Overcoming Biases and Enhancing Collaboration.

JCO clinical cancer informatics·2026
Same author

Repeated Administration of Social and Structural Determinants of Health (SSDOH) Questions in an Alzheimer's Disease Research Center: The Aging Brain Study (ABC) Life Experience Survey.

Sage open aging·2026

Related Experiment Video

Updated: Apr 14, 2026

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

11.1K

Nonparametric discrete survival function estimation with uncertain endpoints using an internal validation subsample.

Jarcy Zee1, Sharon X Xie1

  • 1Department of Biostatistics and Epidemiology, University of Pennsylvania, 607 Blockley Hall, 423 Guardian Drive, Philadelphia, Pennsylvania 19104, U.S.A.

Biometrics
|April 29, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method for survival analysis when true endpoints are missing. The proposed estimator accurately estimates survival functions using both complete and incomplete data, outperforming traditional methods.

Keywords:
Measurement errorMissing dataNonparametric survival analysisUncertain endpointsValidation sample

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

2.8K
Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

9.0K

Related Experiment Videos

Last Updated: Apr 14, 2026

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

11.1K
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

2.8K
Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

9.0K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Medical Data Analysis

Background:

  • Assessing true survival endpoints can be challenging due to cost or invasiveness.
  • Alternative, error-prone endpoints are often collected when true endpoints are unavailable.
  • Existing methods struggle with datasets containing both uncertain and true endpoints.

Purpose of the Study:

  • To develop a statistical method for survival analysis with both uncertain and partially available true endpoints.
  • To propose a nonparametric maximum estimated likelihood estimator for discrete survival functions.
  • To evaluate the performance of the proposed estimator against existing methods.

Main Methods:

  • Developed an estimated likelihood function for data with uncertain and true endpoints.
  • Proposed a nonparametric maximum estimated likelihood estimator for the discrete survival function.
  • Conducted extensive simulations to compare the proposed estimator with naive and complete-case Kaplan-Meier estimators.

Main Results:

  • The proposed estimator is consistent and asymptotically normal.
  • It shows reduced bias compared to the naive Kaplan-Meier estimator (using only uncertain endpoints).
  • It is more efficient than the complete-case Kaplan-Meier estimator (using only true endpoints) with moderate missingness.

Conclusions:

  • The developed method provides a robust approach to survival analysis with partially missing true endpoints.
  • This method offers improved accuracy and efficiency over traditional survival analysis techniques.
  • The approach is applicable to real-world datasets, such as estimating Alzheimer's disease progression.