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

Checking hazard regression models using pseudo-observations.

Maja Pohar Perme1, Per Kragh Andersen

  • 1Department of Biomedical Informatics, University of Ljubljana, Vrazov trg 2, SI-1000 Ljubljana, Slovenia. maja.pohar@mf.uni-lj.si

Statistics in Medicine
|August 21, 2008
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

An Overview and Recent Developments in the Analysis of Multistate Processes.

Statistics in medicine·2026
Same author

Censoring and Competing Risks: Avoidable and Non-Avoidable Events. Comment to the Article "Hazards constitute key quantities for analysing, interpreting and understanding time-to-event data" by Beyersmann, Schmoor, and Schumacher.

Biometrical journal. Biometrische Zeitschrift·2025
Same author

Evaluation of the Slovenian Breast Cancer Screening Programme: Years of Life Gained and Avoided Deaths.

Cancers·2025
Same author

Addressing viral hepatitis C reinfections in a low-threshold programme for people who inject drugs in Slovenia.

Harm reduction journal·2025
Same author

CEUS compared with CECT, MRI, and FDG-PET/CT for diagnosing CRC liver metastases: a diagnostic test accuracy systematic review and meta-analysis.

Expert review of gastroenterology & hepatology·2024
Same author

Cardiovascular events according to blood pressure thresholds recommended by ACC/AHA.

Blood pressure·2024
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
Same journal

Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.

Statistics in medicine·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
See all related articles

This study introduces a general graphical method using pseudo-observations to diagnose survival analysis models, overcoming censoring issues for Cox and additive models. The approach allows for comprehensive assumption checking in both single and multiple covariate scenarios.

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Data Analysis

Background:

  • Graphical methods are crucial for model diagnostics in statistical modeling.
  • Censoring in survival data presents significant challenges for traditional plotting techniques.
  • Existing solutions for censored data are often model-specific.

Purpose of the Study:

  • To develop a general graphical framework for survival model diagnostics applicable to all models.
  • To address the limitations imposed by censoring in survival data analysis.
  • To provide methods for simultaneously checking assumptions of Cox and additive models.

Main Methods:

  • Utilizing pseudo-observations to calculate residuals for each individual at each time point, irrespective of censoring.
  • Developing methods for assessing model assumptions in both single and multiple covariate settings.

Related Experiment Videos

  • Introducing goodness-of-fit tests to complement the diagnostic methods.

    • Main Results:

    • The proposed pseudo-observation method effectively handles censoring in survival data.
    • The framework allows for simultaneous checking of all assumptions for Cox and additive models.
    • The methods are validated using both simulated and real-world datasets.

    Conclusions:

    • The presented general approach using pseudo-observations offers a unified and powerful tool for survival model diagnostics.
    • This method enhances the reliability of survival model fitting by enabling thorough assumption verification.
    • The techniques are broadly applicable and improve the understanding of model performance in the presence of censoring.