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

610
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...
610
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
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

663
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,...
663
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

894
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...
894
Hazard Rate01:11

Hazard Rate

465
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...
465
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

467
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.
467

You might also read

Related Articles

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

Sort by
Same author

How should covariates be handled in randomized trials? Empirical evidence from 50 trials and recommendations for practice.

Journal of clinical epidemiology·2026
Same author

A quantitative framework to assess the potential of earlier cancer detection to improve cancer survival.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

Histotripsy dose impacts tumor cellular damage and treatment outcomes in a preclinical model of hepatocellular carcinoma.

Scientific reports·2026
Same author

Gemcitabine plus nivolumab with carboplatin or oxaliplatin in cisplatin-ineligible patients with metastatic urothelial carcinoma: a randomized phase II trial.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Incidence of hypertension and factors associated with blood pressure control among older adults living with HIV in Western Kenya: a retrospective cohort study.

BMC cardiovascular disorders·2026
Same author

OPERA: a new algorithm for patient stratification based on partially ordered risk factors.

Biometrics·2026

Related Experiment Video

Updated: Feb 28, 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

10.9K

Semiparametric regression on cumulative incidence function with interval-censored competing risks data.

Giorgos Bakoyannis1, Menggang Yu2, Constantin T Yiannoutsos1

  • 1Department of Biostatistics, Fairbanks School of Public Health and School of Medicine, Indiana University, Indianapolis, IN, U.S.A.

Statistics in Medicine
|June 14, 2017
PubMed
Summary

This study addresses competing risks data with interval censoring, common in biomedical research. The new method provides unbiased estimation of cumulative incidence functions, crucial for evaluating interventions and disease prognosis.

Keywords:
R functioncompeting riskscumulative incidence functioninterval censoringsemiparametric efficiency

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
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

692

Related Experiment Videos

Last Updated: Feb 28, 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

10.9K
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
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

692

Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Trials

Background:

  • Biomedical and clinical studies frequently encounter time-to-event outcomes with competing risks.
  • Interval censoring, where event times are known only within observation intervals, is common in such studies.
  • Ignoring interval censoring can bias estimates of the cause-specific cumulative incidence function (CIF).

Purpose of the Study:

  • To develop a robust statistical method for analyzing competing risks data with interval censoring.
  • To estimate the cause-specific cumulative incidence function (CIF) accurately.
  • To provide a flexible modeling framework encompassing existing methods like proportional odds and Fine-Gray models.

Main Methods:

  • Utilized semiparametric generalized odds rate transformation models.
  • Employed sieve maximum likelihood estimation with B-splines for model fitting.
  • Developed an R function (ciregic) for practical implementation.

Main Results:

  • The proposed regression parameter estimator demonstrated consistency, asymptotic normality, and semiparametric efficiency.
  • Simulation studies confirmed the method's good performance, even with small sample sizes.
  • The method was successfully applied to analyze HIV data from a sub-Saharan African cohort study.

Conclusions:

  • The developed method effectively handles interval-censored competing risks data.
  • Accurate estimation of CIF is vital for intervention evaluation, prognosis, and prediction.
  • The provided R function facilitates the application of this advanced statistical technique in research.