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

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

525
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
525
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

592
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...
592
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

195
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.
195
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

296
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...
296
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.3K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.3K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

254
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,...
254

You might also read

Related Articles

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

Sort by
Same author

Gradient boosting-based discrete failure time model for selecting time-varying effects and interactions.

Lifetime data analysis·2026
Same author

State Spending Growth Benchmarks and Hospital Revenue, Hospital Prices, and Premiums.

JAMA network open·2026
Same author

The Extracorporeal Life Support Organization Registry Data Quality and Integrity Program.

ASAIO journal (American Society for Artificial Internal Organs : 1992)·2026
Same author

α/β for Hepatocellular Carcinoma Tumors Treated With Radiation Therapy.

International journal of radiation oncology, biology, physics·2026
Same author

KULLBACK-LEIBLER-BASED DISCRETE FAILURE TIME MODELS FOR INTEGRATION OF PUBLISHED PREDICTION MODELS WITH NEW TIME-TO-EVENT DATASET.

The annals of applied statistics·2026
Same author

Racial and Ethnic Disparities in Occupational Health.

JAMA health forum·2025

Related Experiment Video

Updated: Sep 8, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

A Multiple Imputation Approach for the Cumulative Incidence, with Implications for Variance Estimation.

Elizabeth C Chase1, Philip S Boonstra2, Jeremy M G Taylor2

  • 1RAND Corporation.

The American Statistician
|August 20, 2025
PubMed
Summary

This study introduces a novel multiple imputation method for estimating cumulative incidence functions in competing risks. The approach simplifies complex analyses and offers flexible uncertainty estimation, aligning with established methods.

Keywords:
Competing risksMultiple imputationProportionRedistribute-to-the-rightSurvival analysis

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K
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

2.2K

Related Experiment Videos

Last Updated: Sep 8, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K
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

2.2K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Estimating cumulative incidence in competing risks is crucial for understanding event probabilities.
  • Existing methods like the Aalen-Johansen estimator are widely used but may have limitations.
  • Alternative approaches are needed to enhance flexibility and uncertainty estimation.

Purpose of the Study:

  • To present a new non-parametric multiple imputation method for estimating the cumulative incidence function.
  • To demonstrate the equivalence of this new method to the Aalen-Johansen estimator.
  • To highlight the advantages of the imputation approach for analyzing binary outcomes and estimating uncertainty.

Main Methods:

  • Utilized non-parametric multiple imputation to transform the competing risks problem.
  • Reduced the estimation of the cumulative incidence function to estimating a binomial proportion.
  • Conducted mathematical and empirical analyses to compare with the Aalen-Johansen estimator.

Main Results:

  • The imputation-based estimator was shown to be equivalent to the Aalen-Johansen estimator with sufficient imputations.
  • The proposed method allows for a broader range of statistical techniques for binary outcome analysis.
  • Enhanced options for uncertainty estimation were identified within the new framework.

Conclusions:

  • The novel multiple imputation approach provides a robust alternative for cumulative incidence function estimation.
  • This method offers greater flexibility in statistical analysis and uncertainty quantification.
  • The imputation strategy is potentially extendable to more intricate competing risks scenarios.