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
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

938
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...
938
Survival Curves01:18

Survival Curves

924
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...
924
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

489
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.
489
Hazard Rate01:11

Hazard Rate

521
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...
521
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

785
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,...
785

You might also read

Related Articles

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

Sort by
Same author

From single conventional regression to ensemble modelling: relative importance of the Healthy Eating Index-2015 components in relation to adverse pregnancy outcomes - CORRIGENDUM.

The British journal of nutrition·2026
Same author

From single conventional regression to ensemble modelling: relative importance of the Healthy Eating Index-2015 components in relation to adverse pregnancy outcomes.

The British journal of nutrition·2026
Same author

Combining Observational Studies to Reduce Multiple Biases.

Epidemiology (Cambridge, Mass.)·2026
Same author

Epigenetic inflammation signatures and lung cancer risk among never-smoking women: a nested case-control study.

medRxiv : the preprint server for health sciences·2026
Same author

Ionising radiation and cancer: a UN review of the recent epidemiological evidence.

The Lancet. Oncology·2026
Same author

Effect measure modification in mixtures and public health.

American journal of epidemiology·2026

Related Experiment Video

Updated: Apr 25, 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

9.8K

The parametric g-formula for time-to-event data: intuition and a worked example.

Alexander P Keil1, Jessie K Edwards, David B Richardson

  • 1From the aDepartment of Epidemiology, University of North Carolina, Chapel Hill, NC; and bDepartment of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Québec, Canada.

Epidemiology (Cambridge, Mass.)
|August 21, 2014
PubMed
Summary

The parametric g-formula offers a robust method for estimating treatment effects, accurately adjusting for time-varying confounders. This approach provides a more reliable measure of intervention impact compared to standard regression, crucial for public health policy.

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

10.3K
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

14.3K

Related Experiment Videos

Last Updated: Apr 25, 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

9.8K
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

10.3K
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

14.3K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • The parametric g-formula is a statistical method for estimating intervention effects.
  • It uniquely adjusts for time-varying confounders influenced by prior exposures.
  • Applications in published research remain limited.

Purpose of the Study:

  • To introduce the parametric g-formula.
  • To demonstrate its application in a bone marrow transplant cohort study.
  • To highlight its utility in handling time-varying confounding.

Main Methods:

  • Application of the parametric g-formula to a cohort study.
  • Comparison with standard regression adjustment methods.
  • Analysis focused on treatment effect on mortality.

Main Results:

  • Standard regression adjustment produced biased estimates of treatment effect on mortality.
  • The parametric g-formula provided a less biased estimate.
  • Demonstrated the practical implementation of the g-formula.

Conclusions:

  • The g-formula estimates the hazard of mortality under hypothetical interventions, valuable for public health.
  • It enables assessment of policy impacts, like reducing harmful exposures or introducing new treatments.
  • A simple, adaptable implementation approach is presented for broad public health relevance.