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

793
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...
793
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

478
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...
478
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

234
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.
234
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

352
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
352
Survival Tree01:19

Survival Tree

210
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...
210
Survival Curves01:18

Survival Curves

427
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...
427

You might also read

Related Articles

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

Sort by
Same author

Population Pharmacokinetic and Pharmacodynamic Modeling Analysis of rhIL-7-hyFc, a Hybrid Fc-Fused Long-Acting Interleukin-7, to Support Optimal Dosing Regimens in Patients with Solid Cancer.

Drug design, development and therapy·2025
Same author

Evaluation of drug-drug interaction potentials between JP-1366 and celecoxib using physiologically based pharmacokinetic modeling.

Translational and clinical pharmacology·2025
Same author

PK Modeling of L-4-Boronophenylalanine and Development of Bayesian Predictive Platform for L-4-Boronophenylalanine PKs for Boron Neutron Capture Therapy.

Pharmaceuticals (Basel, Switzerland)·2024
Same author

Pharmacokinetic Interactions Between Bazedoxifene and Cholecalciferol: An Open-Label, Randomized, Crossover Study in Healthy Male Volunteers.

Drug design, development and therapy·2023
Same author

Comparison of Pharmacodynamics between Tegoprazan and Dexlansoprazole Regarding Nocturnal Acid Breakthrough: A Randomized Crossover Study.

Gut and liver·2022
Same author

Pharmacokinetic Drug Interaction Between Amlodipine and Tadalafil: An Open-Label, Randomized, Multiple-Dose Crossover Study in Healthy Male Volunteers.

Drug design, development and therapy·2022

Related Experiment Video

Updated: Nov 9, 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

10.5K

Brief introduction to parametric time to event model.

Hyeong-Seok Lim1

  • 1Department of Clinical Pharmacology and Therapeutics, Asan Medical Center, University of Ulsan, Seoul 05505, Korea.

Translational and Clinical Pharmacology
|April 15, 2021
PubMed
Summary

This tutorial introduces parametric time-to-event (TTE) models, including exponential, Weibull, and log-logistic distributions. Parametric TTE analysis offers advantages over non-parametric methods for evaluating treatment effects and disease prognosis.

Keywords:
Accelerated Failure Time ModelParametric Time to Event ModelProportional Hazard Model

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

2.3K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.8K

Related Experiment Videos

Last Updated: Nov 9, 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

10.5K
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

2.3K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.8K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Pharmacometrics

Background:

  • Time-to-event (TTE) data is crucial for assessing drug treatment efficacy and disease prognosis.
  • Non-parametric Kaplan-Meier analysis is common, but parametric models offer distinct advantages.
  • Parametric models facilitate simulation and the evaluation of continuous covariates.

Purpose of the Study:

  • To explain the fundamental concepts of parametric time-to-event (TTE) models.
  • To highlight commonly used parametric distributions: exponential, Weibull, and log-logistic.
  • To introduce the Accelerated Failure Time (AFT) model and compare it with the Proportional Hazards (PH) model.

Main Methods:

  • Discussion of parametric TTE models including exponential, Weibull, and log-logistic distributions.
  • Introduction of the Accelerated Failure Time (AFT) model for covariate analysis.
  • Comparison of AFT models with Proportional Hazards (PH) models.

Main Results:

  • Parametric models provide advantages in simulation and continuous covariate evaluation.
  • The AFT model offers more intuitive interpretation of covariate effects on TTE compared to the PH model.
  • Covariates directly influence TTE in AFT models, whereas they affect hazard rates in PH models.

Conclusions:

  • Parametric TTE models, particularly exponential, Weibull, and log-logistic, are valuable for survival data analysis.
  • The AFT model presents a more interpretable approach to covariate effects in TTE data compared to the PH model.
  • Understanding these parametric models enhances the analysis of treatment effects and disease prognosis.