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

Censoring Survival Data01:09

Censoring Survival Data

498
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...
498
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

531
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...
531
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

530
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,...
530
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

380
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.
380
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

710
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...
710
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

545
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...
545

You might also read

Related Articles

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

Sort by
Same author

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same author

A Nationwide Assessment of Anticholestatic Therapy Uptake in Patients With Primary Biliary Cholangitis: Opportunities for Optimisation.

Alimentary pharmacology & therapeutics·2026
Same author

SGLT2 Inhibitors, Muscle Loss, and Creatinine-Based Estimated GFR: An Integrative Conceptual Review of Renoprotection.

Kidney medicine·2026
Same author

Comparing the effectiveness of prostate cancer screening protocols: European Association of Urology- and European Randomized Study of Screening for Prostate Cancer-based strategies.

International journal of cancer·2026
Same author

Holter features to detect coronary artery spasm in ANOCA patients: A pilot study.

International journal of cardiology. Heart & vasculature·2026
Same author

Effectiveness and tolerability of bezafibrate in primary biliary cholangitis - a nationwide real-world study.

The American journal of gastroenterology·2025

Related Experiment Video

Updated: Jan 7, 2026

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

2.5K

Time-Dependent Predictive Accuracy Metrics in the Context of Interval Censoring and Competing Risks.

Zhenwei Yang1, Dimitris Rizopoulos1, Lisa F Newcomb2

  • 1Department of Epidemiology and Biostatistics, Erasmus Medical Center Rotterdam, Rotterdam, the Netherlands.

Biometrical Journal. Biometrische Zeitschrift
|January 5, 2026
PubMed
Summary

This study introduces two novel methods for evaluating prediction models with interval-censored time-to-event data, addressing challenges like competing risks. Both model-based and inverse probability of censoring weighting (IPCW) approaches were compared for accuracy metrics.

Keywords:
accuracy metricscompeting risksinterval censoringprediction modeltime‐varying covariates

More Related Videos

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

10.7K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.2K

Related Experiment Videos

Last Updated: Jan 7, 2026

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

2.5K
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

10.7K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.2K

Area of Science:

  • Medical Statistics
  • Survival Analysis
  • Biostatistics

Background:

  • Evaluating prediction model performance is crucial in medical statistics, but standard metrics are challenging with censored time-to-event outcomes.
  • Interval censoring, time-varying covariates, and competing risks further complicate accuracy assessments in survival data.
  • Accurate performance evaluation is essential for reliable clinical predictions and decision-making.

Purpose of the Study:

  • To propose and compare two methods for evaluating prediction models in the presence of interval censoring and competing risks.
  • To assess the performance of model-based and inverse probability of censoring weighting (IPCW) approaches using key time-dependent accuracy metrics.
  • To provide a framework for robust model evaluation in complex survival data settings.

Main Methods:

  • Proposed two distinct approaches: a model-based method and an inverse probability of censoring weighting (IPCW) method.
  • Focused on three time-dependent metrics: area under the receiver-operating characteristic curve (AUC), Brier score, and expected predictive cross-entropy.
  • Conducted a simulation study and applied the methods to a prostate cancer cohort with interval-censored progression and competing events.

Main Results:

  • Both model-based and IPCW approaches demonstrated utility in evaluating prediction models under interval censoring and competing risks.
  • Simulation results provided insights into the comparative performance of the two methods across the selected accuracy metrics.
  • Application to the prostate cancer cohort illustrated the practical implementation and interpretation of the proposed evaluation techniques.

Conclusions:

  • The study offers valuable methods for accurate prediction model evaluation in complex time-to-event data scenarios.
  • Both proposed approaches, model-based and IPCW, provide viable options for assessing model performance with interval censoring and competing risks.
  • These methods enhance the reliability of risk predictions in clinical settings characterized by censored outcomes and competing events.