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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

602
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...
602
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

196
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.
196
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

285
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...
285
Censoring Survival Data01:09

Censoring Survival Data

230
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...
230
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

395
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...
395
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

260
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,...
260

You might also read

Related Articles

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

Sort by
Same author

A more interpretable regression model for count data with excess of zeros.

Statistical methods in medical research·2026
Same author

Sex differences in subjective cognition among middle-aged and older Hispanic/Latino adults: Findings from the HCHS/SOL and SOL-INCA.

The journal of prevention of Alzheimer's disease·2026
Same author

Production of gamma-polyglutamic acid with tunable molecular weight via electrofermentation using soybean protein concentrate as a feedstock.

Bioresource technology·2026
Same author

Corrigendum to "a healthy lifestyle is associated with lower risk of depression in type 2 diabetes, irrespective of genetic susceptibility: A UK biobank cohort study" [J. Affect. Disord. 405 (2026) 121657, doi:10.1016/j.jad.2026.121657].

Journal of affective disorders·2026
Same author

SERS Facemask for Rapid and Portable Sensing Mycobacterium Tuberculosis Antigens for TB Screening.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Linking GWAS risk genes to transcriptional features of major depressive disorder via in vivo Perturb-seq.

Nature genetics·2026

Related Experiment Video

Updated: Sep 10, 2025

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

10.8K

Semiparametric Inference for a Two-Phase Failure-Time-Auxiliary-Dependent Sampling Design.

Xu Cao1, Qingning Zhou2, Jianwen Cai3

  • 1Department of Statistics, University of California at Riverside, Riverside, California, USA.

Statistics in Medicine
|August 22, 2025
PubMed
Summary

This study introduces a cost-effective sampling method, failure-time-auxiliary-dependent sampling (FADS), for epidemiological research. FADS improves efficiency by using auxiliary variables alongside failure times to select participants for expensive exposure measurements.

Keywords:
auxiliary variablenonparametric bootstrapproportional hazards modelsurvival analysistwo‐phase sampling

More Related Videos

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

214
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

10.3K

Related Experiment Videos

Last Updated: Sep 10, 2025

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

10.8K
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

214
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

10.3K

Area of Science:

  • Biostatistics
  • Epidemiological Research Methods
  • Health Economics

Background:

  • Large cohort studies with simple random sampling are often cost-prohibitive for epidemiological research, particularly when exposure variables are expensive or difficult to obtain.
  • Failure-time-dependent sampling (FDS) is a cost-effective strategy for studies with failure times as outcomes, but efficiency can be further improved.

Purpose of the Study:

  • To propose a novel two-phase sampling design, failure-time-auxiliary-dependent sampling (FADS), to enhance study efficiency beyond traditional FDS.
  • To develop statistical methods for unbiased inference and variance estimation under the proposed FADS design.

Main Methods:

  • Introduced a two-phase FADS design where exposure measurement probability depends on failure time and auxiliary variables.
  • Developed a semiparametric maximum pseudo-likelihood approach for statistical inference.
  • Utilized a nonparametric bootstrap procedure for variance estimation to account for sampling bias.

Main Results:

  • The proposed estimator for regression coefficients is statistically consistent and asymptotically normally distributed.
  • Simulation studies demonstrate that the FADS method performs well and is more efficient than competing sampling schemes.
  • The method was successfully applied to analyze data from the ARIC Study and the National Wilms' Tumor Study.

Conclusions:

  • The FADS design offers a more efficient and cost-effective approach for epidemiological studies with expensive exposure variables compared to simple random sampling and FDS.
  • The developed semiparametric inference and bootstrap variance estimation methods provide reliable tools for analyzing data collected under FADS.
  • This methodology has practical implications for conducting budget-constrained epidemiological research.