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

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.5K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.5K
Survival Tree01:19

Survival Tree

504
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...
504
Censoring Survival Data01:09

Censoring Survival Data

688
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...
688
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

770
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,...
770
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.8K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.8K
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

You might also read

Related Articles

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

Sort by
Same author

Quantitative ultrasound scatteromics for characterization of reconstructed neopharyngeal tissue: Assessment of postoperative speech outcome after total laryngopharyngectomy.

Ultrasonics·2026
Same author

Machine-learning prediction of 30-day infection-related hospitalization in advanced CKD.

BMC infectious diseases·2026
Same author

A quantile cure model with partially functional covariate effects.

Statistical methods in medical research·2026
Same author

Interactive active learning for literature screening: finetuning GPT with DeepSeek reasoning for cross-domain generalization.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

Circulating tumor cells (CTCs) enumeration and machine-learning based diagnostic biomarkers for breast cancer detection.

BMC cancer·2026
Same author

Early Detection of Liver Fibrosis Using Scatteromics Based on Multimodal QUS Envelope Statistics Imaging.

Diagnostics (Basel, Switzerland)·2026
Same journal

Comparison of Different Methods for the Meta-Analysis of Diagnostic Test Accuracy Studies-A Simulation Study.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

When to Adjust for Multiple Testing: A Unifying Guiding Principle.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Ensuring Quality in Preclinical Research: The Importance of Being Human.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Addressing Cluster-Level Treatment Effect Heterogeneity in Sample Size Determination for Hierarchical 2 × 2 Factorial Designs.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

A Multiple Imputation Approach to Distinguish Curative From Life-Prolonging Effects in the Presence of Missing Covariates.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach.

Biometrical journal. Biometrische Zeitschrift·2026
See all related articles

Related Experiment Video

Updated: Apr 19, 2026

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

11.2K

Two-stage estimation for multivariate recurrent event data with a dependent terminal event.

Chyong-Mei Chen1, Ya-Wen Chuang, Pao-Sheng Shen

  • 1Department of Statistics and Informatics Science, Providence University, Taichung, 43301, Taiwan, Republic of China; Department of Financial and Computational Mathematics, Providence University, Taichung, 43301, Taiwan, Republic of China.

Biometrical Journal. Biometrische Zeitschrift
|December 20, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible marginal model for analyzing recurrent events, like peritonitis, in patients undergoing peritoneal dialysis. The model accounts for dependent terminal events such as death or hemodialysis, improving analysis of complex longitudinal data.

Keywords:
Dependent terminal eventFrailtyMultivariate recurrent event dataProportional hazards modelTwo-stage estimation

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

11.1K
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

3.8K

Related Experiment Videos

Last Updated: Apr 19, 2026

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

11.2K
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

11.1K
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

3.8K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Data Analysis

Background:

  • Longitudinal studies often involve recurrent event data, where subjects experience multiple occurrences of the same event.
  • Peritonitis in patients on peritoneal dialysis presents a case of multivariate recurrent events with distinct types (Gram-positive and non-Gram-positive).
  • Dependent terminal events, such as death or transition to hemodialysis, complicate the analysis of recurrent events.

Purpose of the Study:

  • To propose a flexible marginal model for analyzing multivariate recurrent event data with dependent terminal events.
  • To address the specific challenges posed by recurrent peritonitis data in patients undergoing peritoneal dialysis.
  • To develop a robust statistical methodology for understanding event recurrence and survival in clinical follow-up studies.

Main Methods:

  • Development of a marginal model incorporating proportional hazard and proportional rates for terminal and recurrent events, respectively.
  • Modeling inter-recurrence dependence and correlations using multiplicative frailties linked to marginal models.
  • A two-stage estimation procedure for parameter estimation, with established consistency.
  • Specification of the recurrent event rate model conditional on the time preceding the terminal event.

Main Results:

  • The proposed flexible marginal model effectively handles multivariate recurrent events with dependent terminal events.
  • Simulation studies demonstrate the appropriateness and practical utility of the developed statistical approach.
  • The methodology was successfully applied to real-world peritonitis cohort data.

Conclusions:

  • The proposed flexible marginal model provides a valuable tool for analyzing complex recurrent event data in clinical settings.
  • The two-stage estimation procedure is consistent and reliable for practical application.
  • This methodology enhances the understanding of disease progression and patient outcomes in longitudinal studies.