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

674
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,...
674
Cancer Survival Analysis01:21

Cancer Survival Analysis

807
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
807
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.2K
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...
1.2K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

677
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...
677
Censoring Survival Data01:09

Censoring Survival Data

612
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...
612
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

472
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.
472

You might also read

Related Articles

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

Sort by
Same author

Improving EU cancer screening policies: A framework for identifying barriers of screening programs with monitoring data.

Journal of cancer policy·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

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

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

Biometrical journal. Biometrische Zeitschrift·2026
Same author

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

The American journal of gastroenterology·2025
Same author

Predicting dropout in intensive longitudinal data: Extending the joint model for autocorrelated data.

Psychological assessment·2025
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
Same journal

Subgroup Analysis of Interval-censored Failure Time Data With Application to Alzheimer's Disease.

Statistics in medicine·2026
Same journal

Rejoinder to Commentaries on "A Perspective on the Appropriate Implementation of ICH E9(R1) Addendum Strategies for Handling Intercurrent Events".

Statistics in medicine·2026
Same journal

A Multi-Stage Drop-the-Loser Design With Superiority Boundaries.

Statistics in medicine·2026
Same journal

Interpretable ROI Identification in Brain Image Analysis: Overcoming CNN Black Box Challenges With Kriging-Enhanced Adaptive Sampling.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Mar 7, 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

11.0K

Personalized Biopsy Schedules Using an Interval-Censored Cause-Specific Joint Model.

Zhenwei Yang1,2, Dimitris Rizopoulos1,2, Eveline A M Heijnsdijk3

  • 1Department of Biostatistics, Erasmus Medical Center Rotterdam, South Holland, the Netherlands.

Statistics in Medicine
|May 26, 2025
PubMed
Summary
This summary is machine-generated.

Personalized biopsy schedules for active surveillance (AS) reduce prostate cancer biopsies by up to 52%. This approach uses an interval-censored cause-specific joint model (ICJM) to tailor testing frequency based on individual risk, minimizing overtreatment.

Keywords:
competing riskdynamic predictioninterval censoringjoint modelsprecision medicine

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

2.7K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

975

Related Experiment Videos

Last Updated: Mar 7, 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

11.0K
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

2.7K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

975

Area of Science:

  • Oncology
  • Biostatistics
  • Medical Informatics

Background:

  • Active surveillance (AS) for prostate cancer reduces overtreatment but relies on fixed biopsy schedules.
  • Current fixed biopsy schedules are not personalized and can lead to patient burden from frequent invasive procedures.
  • The optimal frequency for monitoring cancer progression in AS remains undetermined.

Purpose of the Study:

  • To develop a personalized biopsy scheduling strategy for prostate cancer active surveillance.
  • To model the impact of longitudinal biomarkers on cancer progression while accounting for competing risks.
  • To optimize biopsy timing by balancing the number of procedures and detection delay.

Main Methods:

  • Proposed an interval-censored cause-specific joint model (ICJM) to integrate longitudinal biomarkers and cancer progression.
  • Incorporated interval-censoring, competing risks of early treatment, and uncertainty in progression detection.
  • Developed patient-specific risk profiles to trigger biopsies when a predefined risk threshold is exceeded.

Main Results:

  • The ICJM successfully models cancer progression with competing risks and interval-censoring.
  • Personalized biopsy schedules significantly reduced the number of biopsies per patient by 41%-52% compared to fixed schedules.
  • This reduction in biopsies came with a slight increase in the delay of cancer progression detection.

Conclusions:

  • Personalized biopsy schedules based on patient-specific risk profiles are feasible and effective for prostate cancer AS.
  • The proposed ICJM offers a robust statistical framework for optimizing AS monitoring.
  • This approach has the potential to reduce patient burden and healthcare costs associated with prostate cancer management.