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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

687
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...
687
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

519
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
519
Hazard Ratio01:12

Hazard Ratio

683
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
683
Survival Tree01:19

Survival Tree

460
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
460
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

1.2K
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...
1.2K
Relative Risk01:12

Relative Risk

2.4K
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
2.4K

You might also read

Related Articles

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

Sort by
Same author

Uncovering the heterogeneity of care trajectories leading up to a rheumatoid arthritis diagnosis: a Swedish 10-year study.

Annals of the rheumatic diseases·2026
Same author

A lipidomic based metabolic age score for monitoring the effects of lifestyle and diet on metabolic disease risk.

Research square·2026
Same author

Eigenlipids for exploring lipid biology.

Journal of lipid research·2026
Same author

Socio-Economic Position and the Prevalence of Ten Chronic Diseases in Australia, 2021: A Whole of Population Census Data Analysis.

The Medical journal of Australia·2026
Same author

Comparing dementia prevalence in Australians with and without diabetes across sociodemographic groups: Findings from the 2021 national census.

Journal of Alzheimer's disease : JAD·2026
Same author

Clinical risk score for cardiac death or heart failure hospitalization in moderate aortic stenosis.

Echo research and practice·2026

Related Experiment Video

Updated: Mar 13, 2026

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

2.7K

Comparisons of risk prediction methods using nested case-control data.

Agus Salim1, Bénédicte Delcoigne2, Krystyn Villaflores1

  • 1Mathematics and Statistics, La Trobe University, Bundoora, 3086, VIC, Australia.

Statistics in Medicine
|October 14, 2016
PubMed
Summary
This summary is machine-generated.

Nested case-control (NCC) studies can effectively estimate absolute risk, offering improved statistical efficiency and risk classification compared to traditional methods. This approach provides a cost-effective alternative for developing risk prediction models.

Keywords:
absolute riskcost efficiencyprediction modelsprognosisrisk calculatorstudy design

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

11.0K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

842

Related Experiment Videos

Last Updated: Mar 13, 2026

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

2.7K
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

11.0K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

842

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Estimating absolute risk is crucial for developing and validating risk prediction models.
  • Nested case-control (NCC) studies are cost-effective but their utility for absolute risk estimation has been debated.
  • Previous research suggested limitations in using matched NCC data for absolute risk estimation.

Purpose of the Study:

  • To compare two approaches for estimating absolute risk from NCC data.
  • To demonstrate the feasibility and advantages of using matched NCC data for absolute risk estimation.
  • To provide guidance on obtaining absolute risk estimates from NCC studies.

Main Methods:

  • Utilized both simulated and real-world datasets for analysis.
  • Compared established methods for absolute risk estimation within the NCC design.
  • Focused on matched NCC study data.

Main Results:

  • Demonstrated that matched NCC data can unbiasedly estimate absolute risk.
  • Showed that matched NCC studies offer superior statistical efficiency.
  • Confirmed that matched NCC studies provide more appropriate risk categorization.
  • Contradicted previous findings on the limitations of NCC for absolute risk.

Conclusions:

  • The NCC design is a feasible and advantageous method for estimating absolute risk.
  • Matched NCC studies enhance statistical efficiency and risk classification accuracy.
  • NCC studies present a cost-effective option for risk prediction model development and validation, comparable to full cohort or case-cohort studies.