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

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

126
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.
126
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

235
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...
235
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

186
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...
186
Hazard Rate01:11

Hazard Rate

108
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
108
Crossover Experiments01:16

Crossover Experiments

2.8K
Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
2.8K
Censoring Survival Data01:09

Censoring Survival Data

91
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...
91

You might also read

Related Articles

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

Sort by
Same author

Data-augmented multistate modeling for chronic disease processes using cross-sectional studies: application to HPV and cervical precancer.

Biostatistics (Oxford, England)·2026
Same author

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
Same author

Benefits and Harms of Immediate Versus Delayed Treatment of Cervical Intraepithelial Neoplasia Grade 2 : A Target Trial Emulation.

Annals of internal medicine·2026
Same author

Cessation of Betel Quid Chewing, Smoking, and Alcohol Drinking and Risk of Oral Precancer and Oral Cancer.

JCO global oncology·2026
Same author

Variable Selection for Illness-Death Processes Under Dual Observation Schemes.

Statistics in medicine·2026
Same author

Two-phase designs for cost-effective evaluation of cancer screening tests.

Biometrics·2026

Related Experiment Video

Updated: Jul 1, 2025

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

10.2K

Two-phase designs with failure time processes subject to nonsusceptibility.

Fangya Mao1, Li C Cheung1, Richard J Cook2

  • 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Rockville, MD 20852, United States.

Biometrics
|March 6, 2024
PubMed
Summary

This study introduces efficient two-phase designs for epidemiological studies with costly covariates and long-term survivors. The novel methods improve resource allocation and analysis for complex survival data, outperforming existing subsampling schemes.

Keywords:
EM algorithmcancer riskcure rate mixture modelmissing covariatetwo-phase design

More Related Videos

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.5K
Author Spotlight: Unveiling Mechanisms of Stress Resilience - Significant Findings, Advancements, and Future Research
05:03

Author Spotlight: Unveiling Mechanisms of Stress Resilience - Significant Findings, Advancements, and Future Research

Published on: December 15, 2023

4.0K

Related Experiment Videos

Last Updated: Jul 1, 2025

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

10.2K
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.5K
Author Spotlight: Unveiling Mechanisms of Stress Resilience - Significant Findings, Advancements, and Future Research
05:03

Author Spotlight: Unveiling Mechanisms of Stress Resilience - Significant Findings, Advancements, and Future Research

Published on: December 15, 2023

4.0K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Two-phase designs optimize resource use in epidemiological studies by measuring expensive covariates on a subsample.
  • Existing two-phase designs often rely on the Cox model for right-censored data.
  • Handling long-term survivors due to nonsusceptibility requires advanced statistical modeling.

Purpose of the Study:

  • To develop efficient two-phase design strategies for epidemiological studies with a nonsusceptible fraction of long-term survivors.
  • To propose regression frameworks accommodating mixture models for cure fractions.
  • To introduce novel bivariate residual-dependent designs for complex survival scenarios.

Main Methods:

  • Utilized mixture models incorporating a nonsusceptible fraction.
  • Considered three regression frameworks: logistic cure model, proportional hazards model for susceptibles, and joint models for susceptibility and failure time.
  • Developed a new class of bivariate residual-dependent designs for simultaneous estimation of susceptibility and failure time.

Main Results:

  • The proposed two-phase design strategies demonstrated superior efficiency compared to existing subsampling schemes in simulations.
  • The novel bivariate residual-dependent designs effectively addressed challenges in modeling both susceptibility and failure time.
  • The methods were successfully applied to real-world data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.

Conclusions:

  • The developed two-phase designs offer a statistically robust and resource-efficient approach for epidemiological studies with cure fractions.
  • The new design strategies are particularly valuable for analyzing complex survival data with nonsusceptible populations.
  • This work provides practical tools for improving the analysis of long-term survival data in clinical and epidemiological research.