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...
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.
Censoring Survival Data01:09

Censoring Survival Data

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 reasons...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
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...
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...

You might also read

Related Articles

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

Sort by
Same author

Temporal trends in viral SetPoint and peak CD4 cell count soon after HIV-1 seroconversion.

AIDS (London, England)·2026
Same author

Risk of type 2 diabetes mellitus after treatment with direct-acting antivirals for hepatitis C virus in people with HIV.

AIDS (London, England)·2026
Same author

Psychometric properties of the Flourishing scale in Greek adult population.

BMC psychology·2026
Same author

Remdesivir: Real-World Effectiveness and Safety in Individuals Hospitalized for Non-COVID Reasons and Non-Hospitalized High-Risk Patients During the Omicron Era in Greece.

Microorganisms·2026
Same author

Design and development of an automated surveillance system for outbreak detection and individual risk assessment for HIV and viral hepatitis B and C among people who use drugs: The Hippocrates project.

The International journal on drug policy·2026
Same author

Prospective Associations of Obesity and Obesity Severity With 9 Cardiovascular Outcomes: The Cross-Cohort Collaboration.

Circulation·2026
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

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
See all related articles

Related Experiment Video

Updated: Jun 5, 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

Modelling competing risks data with missing cause of failure.

Giorgos Bakoyannis1, Fotios Siannis, Giota Touloumi

  • 1Department of Hygiene, Epidemiology and Medical Statistics, Athens University Medical School, Athens, Greece. gmbako@med.uoa.gr

Statistics in Medicine
|December 21, 2010
PubMed
Summary
This summary is machine-generated.

Multiple imputation (MI) methods effectively reduce bias in competing risks analysis when cause of failure is missing. Naive methods cause significant bias, while MI provides more accurate parameter estimates and reliable confidence intervals.

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

Related Experiment Videos

Last Updated: Jun 5, 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

Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Competing risks data are common in medical research, where subjects can experience multiple types of failure.
  • Missing cause of failure information complicates analysis, potentially leading to biased results with standard methods.

Purpose of the Study:

  • To investigate the bias of parameter estimates using the Fine and Gray (proportional subdistribution hazards) model with naive methods for missing competing risks data.
  • To evaluate the performance of multiple imputation (MI) methods in analyzing competing risks data with missing cause of failure.

Main Methods:

  • Simulation experiments were conducted to compare parameter estimate bias under different missing data scenarios.
  • The Fine and Gray model was fitted using complete case analysis and treating missing causes as a separate failure type.
  • Multiple imputation (MI) was applied to handle missing cause of failure data.

Main Results:

  • Naive methods (complete case analysis, missing as a separate type) showed substantial bias in parameter estimates when cause of failure was missing at random.
  • The MI-based method yielded estimates with significantly smaller biases compared to naive techniques.
  • MI resulted in 95% confidence interval coverage probabilities closer to the nominal level.

Conclusions:

  • Multiple imputation is a superior method for analyzing competing risks data with missing cause of failure compared to naive approaches.
  • The findings highlight the potential for misleading results when using incomplete data without appropriate imputation techniques.
  • The study applied these methods to real-world data on time to death in HIV-1 infected individuals.