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

Censoring Survival Data01:09

Censoring Survival Data

539
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...
539
Regression Toward the Mean01:52

Regression Toward the Mean

6.9K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.9K
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

269
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
269
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

551
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
551
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

246
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
246
Curing of Concrete01:20

Curing of Concrete

366
The hydration of cement takes place within the water-filled capillary pores. However, environmental elements can disrupt this process by evaporating water from the concrete surfaces. Sealed concrete with a water-cement ratio below 0.5 experiences self-desiccation, leading to water loss. The water loss in concrete is mitigated by curing. This technique involves keeping the concrete saturated to maintain the necessary temperature and moisture conditions, to optimally fill the spaces in the cement...
366

You might also read

Related Articles

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

Sort by
Same author

Automated AI-Based Aortic Measurements From Attenuation Correction CT as an Adjunctive Cardiovascular Risk Biomarker: An International Multicenter Study.

Circulation. Cardiovascular imaging·2026
Same author

High normal parathyroid hormone and cardiovascular and total mortality: sex and age disparities in the ELSA-Brasil study.

The Journal of clinical endocrinology and metabolism·2026
Same author

Lesion-remote astrocytes govern microglia-mediated white matter repair.

Nature·2025
Same author

Histopathological evaluation based on CYP11B2 staining predicts outcomes in unilateral primary aldosteronism.

European journal of endocrinology·2025
Same author

A defective cure rate quantile regression model for male breast cancer data.

Journal of applied statistics·2025
Same author

Concurrent loss of the Y chromosome in cancer and T cells impacts outcome.

Nature·2025

Related Experiment Video

Updated: Jan 27, 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

10.8K

Defective regression models for cure rate modeling with interval-censored data.

Vinicius F Calsavara1, Agatha S Rodrigues2,3, Ricardo Rocha4

  • 1Department of Epidemiology and Statistics, A.C. Camargo Cancer Center, São Paulo, SP, Brazil.

Biometrical Journal. Biometrische Zeitschrift
|March 15, 2019
PubMed
Summary

This study introduces a new cure rate defective model for interval-censored survival data, improving accuracy when event times are not precisely known. The model effectively handles cured patients in survival analysis.

Keywords:
Gompertz distributiondefective distributioninterval-censored datainverse Gaussian distributionlong-term survivor

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

11.0K
Development and Evaluation of a Rat Model of Full-Thickness Cartilage Defects
04:34

Development and Evaluation of a Rat Model of Full-Thickness Cartilage Defects

Published on: May 19, 2023

2.4K

Related Experiment Videos

Last Updated: Jan 27, 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

10.8K
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

11.0K
Development and Evaluation of a Rat Model of Full-Thickness Cartilage Defects
04:34

Development and Evaluation of a Rat Model of Full-Thickness Cartilage Defects

Published on: May 19, 2023

2.4K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Standard survival analysis often uses right-censored data, potentially ignoring interval-censored event times.
  • Ignoring interval censoring can lead to inaccurate event time estimations in survival data.
  • Cure rate models are essential for data with individuals who will never experience the event.

Purpose of the Study:

  • To propose a novel cure rate defective model for interval-censored event-time data.
  • To address limitations in survival analysis where event times fall within intervals.
  • To accurately model data with cured individuals and interval-censored observations.

Main Methods:

  • Development of a defective distribution model for interval-censored data.
  • Utilizing Gompertz and inverse Gaussian defective distributions.
  • Parameter estimation via maximum likelihood estimation (MLE).
  • Performance evaluation through Monte Carlo simulation studies.

Main Results:

  • The proposed cure rate defective model effectively handles interval-censored survival data.
  • Gompertz and inverse Gaussian defective distributions demonstrated utility in modeling cured elements.
  • Monte Carlo simulations confirmed the models' performance.
  • The models showed practical relevance in analyzing cancer recurrence and transplant patient data.

Conclusions:

  • The presented cure rate defective model offers an improved approach for interval-censored survival data.
  • This methodology enhances the analysis of data with potential cure fractions and imprecise event times.
  • The models are applicable to real-world clinical datasets, including cancer and transplantation studies.