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

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...
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...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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,...
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.
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...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

You might also read

Related Articles

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

Sort by
Same author

Hydrogenation of α- and β-Keto Esters under Atmospheric Pressure Catalyzed by a Metal-Ligand Bifunctional Iridium Catalyst.

The Journal of organic chemistry·2026
Same author

Computed Tomography-Based Radiomics Provides New Insights Into Associations Between Pericoronary Fat Characteristics and Low-Density Lipoprotein Cholesterol.

Reviews in cardiovascular medicine·2026
Same author

Lower deep-to-superficial extensor muscle ratio (DSR) as an independent risk factor for early titanium implant subsidence following single-level anterior cervical corpectomy and fusion.

Neurosurgical review·2026
Same author

Incidence and Management of Cerebrospinal Fluid Leakage due to Late Presentation of Dural Tears After Lumbar Surgery.

Orthopaedic surgery·2026
Same author

Chemoselective Semihydrogenation of Azoarenes to Hydrazoarenes under Atmospheric Pressure Catalyzed by a Metal-Ligand Bifunctional Iridium Catalyst.

Inorganic chemistry·2026
Same author

Silent brain infarcts after major cardiac interventions: a retrospective paired cohort study using high-resolution DW-MRI.

BMC cardiovascular disorders·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: May 11, 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

A semiparametric cure model for interval-censored data.

Kwok Fai Lam1, Kin Yau Wong, Feifei Zhou

  • 1Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong. hrntlkf@hku.hk

Biometrical Journal. Biometrische Zeitschrift
|May 31, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new cure model for survival data, accounting for patient health and cured proportions. The model accurately analyzes tumor recurrence and breast retraction, offering improved insights into treatment effects.

Keywords:
Asymptotic normal data augmentationCompound Poisson distributionCure modelInterval-censored dataMultiple imputation

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

Related Experiment Videos

Last Updated: May 11, 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

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Medical Statistics

Background:

  • Survival data analysis often requires accounting for a cured proportion, especially in tumor recurrence studies.
  • Patient health, influenced by covariates like age, sex, and treatment, impacts recurrence time.
  • Existing models may not fully capture the complexities of cured patients and covariate effects.

Purpose of the Study:

  • To propose a novel semiparametric frailty-Cox cure model.
  • To quantify patient health using covariate-dependent frailty with a discrete mass at zero for cured patients.
  • To extend the model for both right-censored and interval-censored survival data.

Main Methods:

  • Developed a semiparametric frailty-Cox cure model with covariate-dependent frailty.
  • Employed a multiple imputation estimation method for right-censored data.
  • Extended the methodology to accommodate interval-censored data.

Main Results:

  • Simulation studies demonstrated highly satisfactory performance of the proposed method.
  • Analysis of melanoma incidence (right-censored) and breast cosmesis (interval-censored) data was performed.
  • The model revealed that radiotherapy with adjuvant chemotherapy increases breast retraction probability but lowers the hazard rate among those experiencing the event.

Conclusions:

  • The proposed frailty-Cox cure model provides a robust framework for analyzing survival data with cured proportions.
  • The model offers nuanced interpretations of treatment effects, distinguishing between the probability of an event and its hazard rate.
  • Findings highlight the importance of cure models in accurately assessing treatment efficacy in oncology and other fields.