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

Hazard Rate01:11

Hazard Rate

The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
Survival Curves01:18

Survival Curves

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

Parametric Survival Analysis: Weibull and Exponential Methods

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...
Kaplan-Meier Approach01:24

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,...
Introduction To Survival Analysis01:18

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...
Assumptions of Survival Analysis01:15

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.

You might also read

Related Articles

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

Sort by
Same author

Real-world heterogeneity in the prognostic value of pre-transplant flow cytometry measurable residual disease in acute myeloid leukemia in first complete remission: CIBMTR analysis.

Haematologica·2026
Same author

Outcomes of myeloablative allogeneic hematopoietic cell transplantation with omidubicel vs alternative donor sources.

Blood neoplasia·2026
Same author

Outcomes of people living with acute lymphoblastic leukemia who received inotuzumab ozogamicin before a stem cell transplant: a plain language summary.

Future oncology (London, England)·2025
Same author

Allogeneic haematopoietic cell transplantation in advanced systemic mastocytosis in the new era: A CIBMTR study.

British journal of haematology·2025
Same author

Causal effect estimation for competing risk data in randomized trial: adjusting covariates to gain efficiency.

Journal of applied statistics·2025
Same author

Cell-free DNA profiles of dermatomyositis and its potential role in discriminating phenotypes.

Frontiers in immunology·2025
Same journal

The systems medicine view of semaglutide: from clinical trials to molecular mechanisms.

Expert review of clinical pharmacology·2026
Same journal

Beyond snapshot dosing: a dynamic living digital twin framework for model-informed precision dosing in critical illness.

Expert review of clinical pharmacology·2026
Same journal

Understanding the relationship between aripiprazole levels in the blood and recurrence of mood episodes in people diagnosed with bipolar I disorder treated with a once-monthly injection of aripiprazole monohydrate: a plain language summary.

Expert review of clinical pharmacology·2026
Same journal

Tirzepatide in type 1 diabetes: beyond mere weight loss.

Expert review of clinical pharmacology·2026
Same journal

Prophylaxis for inherited factor X deficiency: a systematic review.

Expert review of clinical pharmacology·2026
Same journal

Complement C5 inhibitor crovalimab for the treatment of paroxysmal nocturnal hemoglobinuria.

Expert review of clinical pharmacology·2026
See all related articles

Related Experiment Video

Updated: Jun 19, 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

Modeling cumulative incidence function for competing risks data.

Mei-Jie Zhang1, Xu Zhang, Thomas H Scheike

  • 1Division of Biostatistics, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, U.S.A. Tel: +1 414-456-8375;

Expert Review of Clinical Pharmacology
|October 16, 2009
PubMed
Summary
This summary is machine-generated.

This study reviews methods for analyzing competing risks in medical research, focusing on cumulative incidence curves. It compares standard and novel regression techniques for assessing covariate effects on failure rates over time.

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

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

Related Experiment Videos

Last Updated: Jun 19, 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

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

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

Area of Science:

  • Biostatistics
  • Medical Statistics
  • Survival Analysis

Background:

  • Competing risks are common in medical research, where patients face multiple potential causes of failure.
  • Cumulative incidence curves are essential for visualizing failure rates over time for specific causes.
  • Assessing covariate effects on cumulative incidence functions is a key challenge in medical research.

Purpose of the Study:

  • To review and compare standard and novel regression methods for modeling cumulative incidence functions.
  • To provide guidance on assessing covariate effects on cumulative incidence in the presence of competing risks.
  • To illustrate these methods using a real-world bone marrow transplant dataset.

Main Methods:

  • Review of standard regression models for cause-specific hazard rates.
  • Discussion of newer methods including the Fine-Gray subdistribution hazard model.
  • Exploration of direct modeling approaches like pseudovalues and binomial regression.

Main Results:

  • The paper compares the theoretical underpinnings and practical applications of various regression methods.
  • It highlights the availability of computer packages for implementing these statistical models.
  • Analysis of a bone marrow transplant dataset demonstrates the application of different regression techniques.

Conclusions:

  • Both standard and novel regression methods offer valuable tools for analyzing competing risks.
  • The choice of method depends on the specific research question regarding covariate effects on cumulative incidence.
  • Availability of software facilitates the application of these advanced statistical techniques in medical research.