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

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...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
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.
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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...
Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...

You might also read

Related Articles

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

Sort by
Same author

Intermittent mitral regurgitation in Cavalier King Charles spaniels: Short-term progression and influence of stress tests.

Veterinary journal (London, England : 1997)·2020
Same author

Donor Smoking and Older Age Increases Morbidity and Mortality After Lung Transplantation.

Transplantation proceedings·2017
Same author

On adjustment for auxiliary covariates in additive hazard models for the analysis of randomized experiments.

Biometrika·2017
Same author

Mitral Regurgitation Severity and Left Ventricular Systolic Dimension Predict Survival in Young Cavalier King Charles Spaniels.

Journal of veterinary internal medicine·2017
Same author

Genotype-phenotype correlation between the cardiac myosin binding protein C mutation A31P and hypertrophic cardiomyopathy in a cohort of Maine Coon cats: a longitudinal study.

Journal of veterinary cardiology : the official journal of the European Society of Veterinary Cardiology·2016
Same author

Breeding Restrictions Decrease the Prevalence of Myxomatous Mitral Valve Disease in Cavalier King Charles Spaniels over an 8- to 10-Year Period.

Journal of veterinary internal medicine·2015

Related Experiment Video

Updated: Jun 20, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Dynamic path analysis for event time data: large sample properties and inference.

T Martinussen1

  • 1Department of Biostatistics, University of Southern Denmark, J.B. Winslows Vej 9B, 5000 Odense C, Denmark. tmartinussen@health.sdu.dk

Lifetime Data Analysis
|August 25, 2009
PubMed
Summary

This study explores how initial treatments affect survival endpoints, considering time-varying covariates. It details methods to estimate indirect treatment effects and validates them with simulations and real-world data analysis.

More Related Videos

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
06:37

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

Related Experiment Videos

Last Updated: Jun 20, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
06:37

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Investigating treatment effects on survival endpoints is crucial.
  • Time-varying covariates can influence survival outcomes and treatment effects.
  • The impact of dynamic covariates on treatment efficacy requires careful consideration.

Purpose of the Study:

  • To develop and validate methods for estimating the indirect effect of an initial treatment on survival endpoints, mediated by a time-varying covariate.
  • To extend dynamic path analysis concepts to survival data under different statistical models.
  • To provide tools for inferring treatment effects in the presence of complex covariate interactions.

Main Methods:

  • Utilized the Aalen additive hazards model and dynamic path analysis for effect decomposition.
  • Derived large sample properties for the cumulative indirect effect estimator.
  • Employed Monte Carlo simulations for small sample performance evaluation.
  • Applied the Cox model to recurrent events data, demonstrating similar effect decomposition.

Main Results:

  • Established the theoretical basis for decomposing total treatment effects into direct and indirect components.
  • Provided statistical properties for the indirect effect estimator, enabling inference.
  • Demonstrated the applicability of the decomposition approach to recurrent events data using the Cox model.
  • Illustrated the methods with two practical data applications.

Conclusions:

  • The proposed methods allow for robust estimation and inference of indirect treatment effects in survival analysis.
  • Dynamic path analysis provides a valuable framework for understanding complex treatment-covariate-endpoint relationships.
  • The decomposition of treatment effects is applicable across different survival models, including the Cox model for recurrent events.