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 Experiment Videos

Explained variation in survival analysis

M Schemper1, J Stare

  • 1Department of Medical Computer Sciences, Vienna University, Austria.

Statistics in Medicine
|October 15, 1996
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Statistical controversies in clinical research: the importance of importance.

Annals of oncology : official journal of the European Society for Medical Oncology·2016
Same author

Parameters influencing FVIII pharmacokinetics in patients with severe and moderate haemophilia A.

Haemophilia : the official journal of the World Federation of Hemophilia·2015
Same author

Prevention of ischemic events in patients with peripheral arterial disease design, baseline characteristics and 2-year results an observational study.

International angiology : a journal of the International Union of Angiology·2012
Same author

Lack of impact of platinum dose intensity on the outcome of ovarian cancer patients. 10-year results of a prospective randomised phase III study comparing carboplatin-cisplatin with cyclophosphamide-cisplatin.

European journal of cancer (Oxford, England : 1990)·2003
Same author

Alcohol, tobacco and breast cancer--collaborative reanalysis of individual data from 53 epidemiological studies, including 58,515 women with breast cancer and 95,067 women without the disease.

British journal of cancer·2002
Same author

Supporting discovery in medicine by association rule mining in Medline and UMLS.

Studies in health technology and informatics·2001
Same journal

Latent Class Log-Linear Models for Estimating Diagnostic Test Accuracy Without a Gold Standard: A Simulation Study.

Statistics in medicine·2026
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
See all related articles

Researchers compared explained variation measures for Cox regression models. No single measure is best; choices depend on whether data is uncensored or censored, with specific recommendations for each scenario.

Area of Science:

  • Biostatistics
  • Survival Analysis

Background:

  • Cox proportional hazards regression is widely used for survival data analysis.
  • Quantifying explained variation in Cox models is crucial for interpreting model fit and predictive accuracy.
  • Existing measures of explained variation lack a standardized approach and clear performance guidelines.

Purpose of the Study:

  • To categorize and compare various measures of explained variation for Cox proportional hazards models.
  • To evaluate the performance of these measures against established criteria for survival data.
  • To provide guidance on selecting appropriate explained variation measures based on data characteristics.

Main Methods:

  • Categorization of explained variation measures into three classes based on general linear model R2 definitions.

Related Experiment Videos

  • Empirical comparison of measure performance using a set of defined criteria.
  • Application of a data augmentation algorithm for multiple imputation under proportional hazards for censored data.

    • Main Results:

    • No single measure of explained variation demonstrated uniform superiority across all scenarios.
    • For uncensored populations, Kent and O'Quigley's measure and squared rank correlation are recommended.
    • For censored populations, Schemper's measure (V2) is a potential candidate.

    Conclusions:

    • The choice of explained variation measure for Cox models is context-dependent, particularly concerning data censoring.
    • Routinely evaluating explained variation is encouraged in survival data studies.
    • Further research may be needed to develop universally applicable and robust measures.