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

Multiple Regression01:25

Multiple Regression

3.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.2K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

717
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
717
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

298
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
298
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

309
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...
309
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

198
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.
198
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

289
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...
289

You might also read

Related Articles

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

Sort by
Same author

Correction: Accorsi Buttini et al. Development of a Simplified Geriatric Score-4 (SGS-4) to Predict Outcomes After Allogeneic Hematopoietic Stem Cell Transplantation in Patients Aged over 50. <i>Cancers</i> 2025, <i>17</i>, 3278.

Cancers·2026
Same author

A phase II multicenter prospective study to evaluate the safety and efficacy of myeloablative-dose treosulfan plus fludarabine (FT14) conditioning regimen in allogeneic hematopoietic stem cell transplantation for AML patients aged 40-65 in first complete remission.

Bone marrow transplantation·2026
Same author

Predictive averaging and Rubin's rule-based model pooling to predict survival risk with imputations in the presence of missing patient data: methodology and verification using two case studies and simulations.

BMC medical research methodology·2026
Same author

Occult hepatitis B virus infection status is not associated with impaired efficacy of anti-CD19 CAR-T-cell therapy for lymphoma and shows a distinct immune toxicity profile: Results from the CART-SIE study.

HemaSphere·2026
Same author

Dynamics of infection, vaccination and excess mortality during the COVID-19 pandemic among older individuals-a nationwide analysis.

European journal of epidemiology·2026
Same author

A Bayesian Location-Scale Joint Model for Time-To-Event and Multivariate Longitudinal Data With Association Based on Within-Individual Variability.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Sep 14, 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

Multiple Imputation of Missing Covariates When Using the Fine-Gray Model.

Edouard F Bonneville1, Jan Beyersmann2, Ruth H Keogh3

  • 1Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands.

Statistics in Medicine
|July 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new multiple imputation method for the Fine-Gray model, improving covariate analysis with competing risks. The approach enhances efficiency and accuracy in estimating risks, especially when data are incomplete.

Keywords:
Fine–Gray modelcompeting riskscumulative incidence functionmissing covariatesmultiple imputationsubdistribution hazard

More Related Videos

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.3K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.3K

Related Experiment Videos

Last Updated: Sep 14, 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
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.3K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.3K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • The Fine-Gray model is crucial for analyzing competing risks in survival data.
  • Missing covariate data pose challenges in accurately estimating these risks.
  • Existing imputation methods may not align with the Fine-Gray model's assumptions.

Purpose of the Study:

  • To develop a multiple imputation method compatible with the Fine-Gray model for competing risks.
  • To address missing covariate data in the context of estimating the risk of a single event.
  • To improve the efficiency and accuracy of covariate association estimation.

Main Methods:

  • Developed a novel multiple imputation approach leveraging parallels between Fine-Gray and Cox models.
  • Incorporated imputation of potential censoring times for competing events.
  • Utilized existing Cox model imputation methodology for missing covariates.

Main Results:

  • The proposed method demonstrated good performance in estimating subdistribution log hazard ratios and cumulative incidences.
  • It showed efficiency gains over complete-case analysis in simulations and a real-world example.
  • Performance was satisfactory even when the proportional subdistribution hazards assumption was not strictly met.

Conclusions:

  • The new imputation method is effective for the Fine-Gray model with missing covariate data.
  • Accurate specification of proportionality on the correct scale is vital for individual cumulative incidence estimation.
  • This approach offers a valuable tool for researchers analyzing competing risks data.