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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

833
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...
833
Survival Tree01:19

Survival Tree

250
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
250
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

259
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.
259
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

532
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...
532
Survival Curves01:18

Survival Curves

474
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
474
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

412
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...
412

You might also read

Related Articles

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

Sort by
Same author

Trustworthy Artificial Intelligence in Health Requires Public Health Leadership.

American journal of public health·2026
Same author

The association between naloxone distribution, buprenorphine treatment and retention and incident high-risk opioid prescribing with opioid overdose death in Kentucky, Massachusetts, New York and Ohio, United States: An exploratory community-level cohort study of data from the HEALing Communities Study.

Addiction (Abingdon, England)·2026
Same author

Statins and survival free of incident frailty among older US veterans.

European heart journal·2026
Same author

Gabapentin to achieve HIV viral load suppression in people with risky drinking in Mbarara, Uganda: study protocol for a randomized, double-blinded, placebo-controlled trial (GRAIL).

Trials·2026
Same author

COVID-19 pandemic-related stress and substance use behaviors among people with HIV - a mixed method analysis.

PloS one·2026
Same author

Pulmonary rehabilitation is associated with increased 1-year survival in stable chronic obstructive pulmonary disease.

American journal of respiratory and critical care medicine·2026

Related Experiment Video

Updated: Nov 26, 2025

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

10.6K

GENERATING SURVIVAL TIMES WITH TIME-VARYING COVARIATES USING THE LAMBERT W FUNCTION.

Julius S Ngwa1,2, Howard J Cabral1, Debbie M Cheng1

  • 1Department of Biostatistics, Boston University, School of Public Health, 801 Massachusetts Ave, CT 3 Floor, Boston, MA 02118, U.S.A.

Communications in Statistics: Simulation and Computation
|December 14, 2020
PubMed
Summary

This study introduces a new method for simulating time-to-event data using the Cox proportional hazard model with time-varying covariates. This approach enhances the reliability of survival analysis simulation studies.

Keywords:
Lambert W FunctionLinear Mixed Effects ModelLongitudinal and Survival DataTime-varying CovariatesTwo Step Approach

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

8.6K

Related Experiment Videos

Last Updated: Nov 26, 2025

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

10.6K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

8.6K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Simulation studies are crucial for evaluating survival analysis methods.
  • Existing methods for simulating time-to-event data often lack robust approaches for time-varying covariates within the Cox proportional hazard model.
  • There is a significant need for reliable data-generating processes for complex survival models.

Purpose of the Study:

  • To develop and describe a novel method for generating time-to-event data under the Cox proportional hazard model.
  • To specifically address the simulation of data incorporating time-varying covariates.
  • To provide a robust framework for simulation studies involving Exponential and Weibull survival distributions.

Main Methods:

  • Derived closed-form expressions for generating survival times under Exponential and Weibull distributions.
  • Integrated continuous time-varying covariates and time-invariant covariates into the data generation process.
  • Developed an approach to link time-varying covariates with the hazard function within the Cox model framework.

Main Results:

  • Successfully generated time-to-event data incorporating time-varying covariates for Cox-Exponential and Cox-Weibull models.
  • The proposed method demonstrated reliable and robust estimation of association parameters in simulation scenarios.
  • The approach accommodates continuous time-varying covariates measured at regular intervals.

Conclusions:

  • The presented method offers a valuable tool for conducting more accurate and reliable simulation studies in survival analysis.
  • This approach addresses a critical gap in simulating data for Cox proportional hazard models with time-varying covariates.
  • The findings support the use of this method for robust statistical inference in time-to-event data analysis.