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

501
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...
501
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

233
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...
233
Hazard Rate01:11

Hazard Rate

148
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...
148
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

163
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.
163
Sampling Plans01:23

Sampling Plans

225
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
225
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

452
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
452

You might also read

Related Articles

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

Sort by
Same author

Harmonic Fowlkes-Mallows Index for Medical Diagnostics Tests and Optimal Cut-Off Point Selection of Binary Diseases.

Pharmaceutical statistics·2026
Same author

Cutting Carbon with Knife and Bin: The Role of Diet and Food Recycling in the Food System of Ulaanbaatar, Mongolia.

Foods (Basel, Switzerland)·2026
Same author

Uniform nanoporous zirconia composite membrane enabling high-performance alkaline water electrolysis.

Nature communications·2026
Same author

Diagnostic Accuracy Measures and Optimal Cut-Off Points for Nested Disease Subtypes Within the Disease Ordinal States.

Pharmaceutical statistics·2026
Same author

Gradient elevation of serum CYFRA21-1 and its synergy with KL-6 for risk stratification in rheumatoid arthritis-associated interstitial lung disease.

Frontiers in medicine·2026
Same author

Spatial tumor evolution panorama of ovarian cancer.

Cell reports. Medicine·2026

Related Experiment Video

Updated: Aug 1, 2025

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

2.1K

On Cox proportional hazards model performance under different sampling schemes.

Hani Samawi1, Lili Yu1, JingJing Yin1

  • 1Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia, United States of America.

Plos One
|April 26, 2023
PubMed
Summary
This summary is machine-generated.

New sampling methods, Extreme Ranked Set Sampling (ERSS) and Double Extreme Ranked Set Sampling (DERSS), improve survival data analysis. These methods offer more powerful tests and efficient estimates compared to simple random sampling.

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

10.3K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K

Related Experiment Videos

Last Updated: Aug 1, 2025

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

2.1K
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
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Cox's proportional hazards (PH) model is a standard for survival data analysis.
  • Efficient sampling is crucial for accurate time-to-event data analysis.
  • Baseline variables associated with survival time can guide observation selection.

Purpose of the Study:

  • To evaluate the performance of Extreme Ranked Set Sampling (ERSS) and Double Extreme Ranked Set Sampling (DERSS) for Cox's PH models.
  • To compare ERSS and DERSS with Simple Random Sampling (SRS) in survival data analysis.
  • To assess the efficiency and power of different sampling schemes.

Main Methods:

  • Intensive simulations were conducted to compare sampling schemes.
  • Theoretical analysis of Fisher's information was performed for DERSS, ERSS, and SRS.
  • The SEER Incidence Data was used for practical illustration.

Main Results:

  • ERSS and DERSS provide more powerful testing procedures than SRS.
  • ERSS and DERSS yield more efficient estimates of the hazard ratio.
  • Theoretical analysis confirmed Fisher's information: DERSS > ERSS > SRS.

Conclusions:

  • Modified ERSS and DERSS are superior sampling schemes for survival data analysis using Cox's PH models.
  • These methods offer significant improvements in statistical power and estimation efficiency.
  • The proposed sampling techniques are cost-effective, enhancing survival data analysis.