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Related Concept Videos

Survival Tree01:19

Survival Tree

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 survival tree begins...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
Three-Compartment Open Model01:06

Three-Compartment Open Model

The three-compartment open model is a pharmacokinetic model used to describe the distribution and elimination of drugs following extravascular administration. It comprises a central compartment representing the plasma and two peripheral compartments. The highly perfused peripheral compartment represents organs and tissues with a rich blood supply, such as the liver, kidneys, and lungs. The scarcely perfused peripheral compartment represents tissues with lower blood supply, such as adipose...
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Compartment Models: Two-Compartment Model01:20

Compartment Models: Two-Compartment Model

The two-compartment model divides the body into central and peripheral compartments to account for varying blood perfusion rates among organs and tissues, affecting drug distribution. The central compartment includes blood and highly perfused tissues with rapid drug distribution, while the peripheral compartment contains tissues with slower drug distribution. After a single IV bolus dose, the drug concentration is high in plasma and low in tissues. The drug distribution between compartments...

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Related Experiment Video

Updated: Jun 9, 2026

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
05:22

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies

Published on: May 9, 2019

A Nested Copula Model for Recurrent Gap Times With a Dependent Terminal Event.

Yuanjia Duan1, Miao Han1, Liuquan Sun2,3

  • 1School of Statistics and Data Science, Shanghai University of Finance and Economics, Shanghai, P.R. China.

Statistics in Medicine
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel nested copula model to analyze recurrent events and terminal events in clinical trials. The model effectively captures complex correlations, offering improved interpretability for covariate effects.

Keywords:
joint modelingnested copulasrecurrent eventterminal event

Related Experiment Videos

Last Updated: Jun 9, 2026

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
05:22

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies

Published on: May 9, 2019

Area of Science:

  • Biostatistics
  • Clinical Trial Analysis
  • Survival Analysis

Background:

  • Clinical trials frequently involve recurrent events and terminal events, complicating data analysis.
  • Understanding covariate effects on recurrent event gap times and their correlation with terminal events is crucial.

Purpose of the Study:

  • To propose a novel nested copula model for analyzing recurrent and terminal events in clinical trials.
  • To investigate the effect of covariates on recurrent gap times and their dependence with terminal events.
  • To provide a copula-based alternative to frailty models with enhanced interpretability.

Main Methods:

  • Development of a two-layer nested copula model: Archimedean copula for internal correlations, bivariate copula for dependence with terminal events.
  • Parametric and semiparametric methods for parameter estimation.
  • A likelihood-based copula selection procedure for model choice.

Main Results:

  • The proposed nested copula model effectively captures correlations among recurrent and terminal events.
  • Parametric and semiparametric estimators are consistent and asymptotically normal.
  • Simulation studies confirm the finite sample properties of the developed methods.

Conclusions:

  • The nested copula model offers a flexible and interpretable framework for analyzing complex event data in clinical trials.
  • The methods provide reliable parameter estimation and model selection capabilities.
  • The approach is applicable to real-world clinical data, as demonstrated with colorectal cancer data.