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

1.3K
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...
1.3K
Hazard Rate01:11

Hazard Rate

537
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...
537
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

441
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
441
Exponential Equations for Modeling Growth01:26

Exponential Equations for Modeling Growth

461
Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
461
Pole and System Stability01:24

Pole and System Stability

1.3K
The transfer function is a fundamental concept representing the ratio of two polynomials. The numerator and denominator encapsulate the system's dynamics. The zeros and poles of this transfer function are critical in determining the system's behavior and stability.
Simple poles are unique roots of the denominator polynomial. Each simple pole corresponds to a distinct solution to the system's characteristic equation, typically resulting in exponential decay terms in the system's...
1.3K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

335
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
335

You might also read

Related Articles

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

Sort by
Same author

The Impacts of HIV-Related Service Interruptions During the COVID-19 Pandemic: Protocol of a Mixed Methodology Longitudinal Study.

AIDS and behavior·2023
Same author

Spatial Distribution of Parvalbumin-Positive Fibers in the Mouse Brain and Their Alterations in Mouse Models of Temporal Lobe Epilepsy and Parkinson's Disease.

Neuroscience bulletin·2023
Same author

The incidence and dynamic risk factors of chronic kidney disease among people with HIV.

AIDS (London, England)·2023
Same author

Association between clusters of antibodies against periodontal microorganisms and Alzheimer disease mortality: Evidence from a nationally representative survey in the USA.

Journal of periodontology·2023
Same author

CC Chemokine 2 Promotes Ovarian Cancer Progression through the MEK/ERK/MAP3K19 Signaling Pathway.

International journal of molecular sciences·2023
Same author

Hybridized Triboelectric-Electromagnetic Aeolian Vibration Generator as a Self-Powered System for Efficient Vibration Energy Harvesting and Vibration Online Monitoring of Transmission Lines.

ACS applied materials & interfaces·2023

Related Experiment Video

Updated: May 5, 2026

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

2.9K

Accelerated hazards model based on parametric families generalized with Bernstein polynomials.

Yuhui Chen1, Timothy Hanson, Jiajia Zhang

  • 1Department of Statistics, University of South Carolina, Columbia, South Carolina, U.S.A.

Biometrics
|November 23, 2013
PubMed
Summary

We introduce a new Bayesian nonparametric prior for the accelerated hazards model, improving density estimation and covariate analysis. This flexible approach enhances accuracy in statistical modeling for complex data, outperforming existing methods in simulations.

Keywords:
Accelerated hazards modelBayesian nonparametric priorSurvival analysisTime dependent covariate

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

9.9K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.9K

Related Experiment Videos

Last Updated: May 5, 2026

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

2.9K
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

9.9K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.9K

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Accelerated hazards models are crucial for analyzing time-to-event data.
  • Existing methods may lack flexibility in density estimation and covariate handling.
  • Bayesian nonparametric methods offer advantages in modeling complex data distributions.

Purpose of the Study:

  • To propose a novel Bayesian nonparametric prior for the accelerated hazards model.
  • To enhance density estimation and accommodate time-dependent covariates.
  • To evaluate the performance of the proposed method against existing approaches.

Main Methods:

  • A transformed Bernstein polynomial centered at standard parametric families (Weibull, log-logistic) is utilized.
  • The approach blends parametric and nonparametric methods for flexible density estimation.
  • Standard optimization techniques in SAS or R are employed for posterior mode and covariance estimation.

Main Results:

  • The proposed method demonstrates superior performance in simulation studies compared to previous approaches.
  • The generalized model effectively incorporates time-dependent covariates.
  • The approach provides a robust framework for Bayesian nonparametric inference in survival analysis.

Conclusions:

  • The novel Bayesian nonparametric prior offers a flexible and accurate tool for accelerated hazards modeling.
  • This method improves upon existing techniques, particularly for complex data with time-dependent covariates.
  • The approach is applicable to real-world scenarios, such as analyzing cancer treatment effectiveness.