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

Predictive accuracy and explained variation in Cox regression.

M Schemper1, R Henderson

  • 1Department of Medical Computer Sciences, Vienna University, Austria. Michael.Schemper@akh-wien.ac.at

Biometrics
|April 28, 2000
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

First Measurement of Time-Dependent CP Violation in the Flavor-Changing Neutral-Current Decay B^{0}→K_{S}^{0}μ^{+}μ^{-}.

Physical review letters·2026
Same author

Measurement of the Top-Quark Production Cross Section and Charge Asymmetry at LHCb.

Physical review letters·2026
Same author

Targeted degradation of CBP/p300 by CBPD-409 exhibits robust anti-myeloma activity.

Leukemia·2026
Same author

Searches for B^{0}→K^{+}π^{-}τ^{+}τ^{-} and B_{s}^{0}→K^{+}K^{-}τ^{+}τ^{-} Decays.

Physical review letters·2026
Same author

First Evidence of the B_{s}^{0}→K^{-}π^{+}γ Decay.

Physical review letters·2026
Same author

Precision Measurement of CP Violation and Branching Fractions in B^{±}→K_{S}^{0}h^{±} (h=π, K) Decays and Search for the Rare Decay B_{c}^{±}→K_{S}^{0}K^{±}.

Physical review letters·2026
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

A new measure, V, quantifies survival model predictive accuracy, improving censored data analysis. Graphical methods aid interpretation, revealing often low predictive accuracy despite significant prognostic factors.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Medical Statistics

Background:

  • Assessing the predictive accuracy of proportional hazards models is crucial in survival analysis.
  • Existing measures may not adequately handle censored survival data.
  • Understanding the proportion of variation explained by covariates is key for model interpretation.

Purpose of the Study:

  • To introduce a novel measure, V, for quantifying the proportion of variation in censored survival times explained by proportional hazards models.
  • To enhance the handling of censored data compared to previous measures like V1.
  • To provide complementary measures (Dx and D) for assessing absolute predictive accuracy.

Main Methods:

  • Developed a new statistic, V, based on distance measures between observed and fitted survival curves.

Related Experiment Videos

  • Contrasted distance measures (Dx and D) with and without covariate information to assess absolute predictive accuracy.
  • Recommended graphical comparisons of survival curves for prognostic index groups for enhanced interpretation.

    • Main Results:

    • The proposed measure V offers improved handling of censored survival data.
    • Distance measures Dx and D provide insights into absolute predictive accuracy.
    • Analysis of lung cancer and other clinical datasets indicates that predictive accuracy is often low, even with significant prognostic factors.

    Conclusions:

    • The new measure V and associated distance measures offer valuable tools for evaluating proportional hazards models.
    • Graphical methods are recommended to facilitate the understanding and interpretation of model performance.
    • Even statistically significant prognostic factors may contribute to limited overall predictive accuracy in survival models.