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

Weighted Mean00:57

Weighted Mean

6.2K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
6.2K
Relative Risk01:12

Relative Risk

2.0K
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
2.0K
Mass and Weight01:19

Mass and Weight

15.2K
Mass and weight are often used interchangeably in everyday conversation. For example,  medical records often show our weight in kilograms, but never in the correct units of newtons. In physics, however, there is an important distinction. Weight is the pull of the Earth on an object. It depends on the distance from the center of the Earth. Weight dramatically varies if we leave the Earth's surface, unlike mass, which does not vary with location. On the Moon, for example, the...
15.2K
Atomic Weight01:25

Atomic Weight

11.8K
Protons and neutrons have approximately the same mass, about 1.67 × 10-24 grams. Scientists arbitrarily define this amount of mass as one atomic mass unit (amu) or one Dalton. Electrons are much smaller in mass than protons, weighing only 9.11 × 10-28 grams, or about 1/1800 of an atomic mass unit. As a result, they do not contribute much to an element's overall atomic mass. This means that, when considering atomic mass, it is customary to ignore the mass of any electrons and...
11.8K
Apparent Weight01:09

Apparent Weight

9.8K
True weight is the measure of the gravitational force acting on an object. However, if the object accelerates, its measured weight is different from its true weight. Similar observations can be made when the object is submerged in water. An object's weight in water is its apparent weight, which is equal to the difference between its true weight and the buoyant forces.
Consider a person standing on a bathroom scale inside an elevator. If the scale is accurate at rest, its reading equals the...
9.8K
Cable Subjected to Its Own Weight01:13

Cable Subjected to Its Own Weight

786
Overhead power transmission lines rely on cables to carry electricity across large distances. To ensure the stability and functionality of these lines, it is crucial to understand the shape and tension experienced by the cables under the influence of their weight.
A generalized loading function is employed to analyze a cable subjected to its own weight. This function considers the force acting along the cable's arc length rather than its projected length, providing a more accurate...
786

You might also read

Related Articles

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

Sort by
Same author

A nonparametric dependent competing risk method for net survival analysis.

The international journal of biostatistics·2026
Same author

Design characteristics of sequential multiple assignment randomised trials (SMARTs) for human health: a scoping review of studies between 2009 and 2024.

BMJ open·2025
Same author

Prediction of transition probabilities in multi-state models with nested case-control data.

Biometrics·2025
Same author

Effects Among the Affected.

Statistics in medicine·2025
Same author

Dynamic prediction by landmarking with data from cohort subsampling designs.

Statistical methods in medical research·2025
Same author

CHARM is prognostic of geriatric morbidity and toxicity after allogeneic transplant for older adults: BMT CTN 1704 study.

Blood advances·2025
Same journal

Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.

Journal of the American Statistical Association·2026
Same journal

Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.

Journal of the American Statistical Association·2026
Same journal

Dimension Reduction for Large-Scale Federated Data: Statistical Rate and Asymptotic Inference.

Journal of the American Statistical Association·2026
Same journal

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.

Journal of the American Statistical Association·2026
Same journal

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data.

Journal of the American Statistical Association·2026
Same journal

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026
See all related articles

Related Experiment Video

Updated: Jan 25, 2026

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

2.5K

Weighted NPMLE for the Subdistribution of a Competing Risk.

Anna Bellach1, Michael R Kosorok2, Ludger Rüschendorf3

  • 1Department of Biostatistics at University of Copenhagen.

Journal of the American Statistical Association
|May 11, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new weighted likelihood method for analyzing competing risks data, extending the Fine-Gray model. This approach effectively models time-dependent covariate effects on subdistribution hazards, offering improved analysis for complex event data.

Keywords:
Fine-Gray modelcumulative incidence functionnonparametric maximum likelihood estimationsemiparametric transformation modelstime-varying covariates

More Related Videos

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

10.8K
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

660

Related Experiment Videos

Last Updated: Jan 25, 2026

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

2.5K
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

10.8K
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

660

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Analyzing data with multiple, competing event types is crucial in many fields.
  • Existing regression methods for subdistribution analysis often rely on non-likelihood-based procedures.
  • There is a need for direct regression modeling of subdistribution effects, especially with time-dependent covariates.

Purpose of the Study:

  • To introduce a novel weighted likelihood function for direct regression modeling of the subdistribution.
  • To extend the Fine-Gray model to a broader class of semiparametric regression models.
  • To accommodate time-dependent covariate effects on the subdistribution hazard.

Main Methods:

  • Development of a weighted likelihood function for semiparametric regression models.
  • Derivation of standard nonparametric estimators and a new interpretation using pseudo risk sets.
  • Establishment of consistency and asymptotic normality for the proposed estimators.
  • Proposal of a sandwich estimator for variance estimation.

Main Results:

  • The weighted likelihood method provides a direct extension of the Fine-Gray model.
  • The proposed model effectively handles time-dependent covariate effects on subdistribution hazards.
  • Simulation studies demonstrate the robust performance of the weighted non-parametric maximum likelihood estimator (NPMLE) under independent right censoring.
  • The method shows practical utility in analyzing large datasets, such as bone marrow transplant data.

Conclusions:

  • The novel weighted likelihood approach offers a powerful tool for analyzing competing risks data.
  • This method allows for direct modeling of subdistribution hazards with time-dependent covariates.
  • The approach is statistically sound, with proven consistency and asymptotic normality.
  • The practical application highlights its value in real-world epidemiological and clinical research.