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

Modelling competing risks in cancer studies.

John P Klein1

  • 1Division of Biostatistics, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA. klein@mcw.edu

Statistics in Medicine
|January 26, 2006
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

Comparing Outcomes with Bone Marrow or Peripheral Blood Stem Cells as Graft Source for Matched Sibling Transplants in Severe Aplastic Anemia across Different Economic Regions.

Biology of blood and marrow transplantation : journal of the American Society for Blood and Marrow Transplantation·2016
Same author

The Impact of Graft-versus-Host Disease on the Relapse Rate in Patients with Lymphoma Depends on the Histological Subtype and the Intensity of the Conditioning Regimen.

Biology of blood and marrow transplantation : journal of the American Society for Blood and Marrow Transplantation·2015
Same author

Center for International Blood and Marrow Transplant Research chronic graft-versus-host disease risk score predicts mortality in an independent validation cohort.

Biology of blood and marrow transplantation : journal of the American Society for Blood and Marrow Transplantation·2014
Same author

Impact of Chronic Graft-versus-Host Disease on Late Relapse and Survival on 7,489 Patients after Myeloablative Allogeneic Hematopoietic Cell Transplantation for Leukemia.

Clinical cancer research : an official journal of the American Association for Cancer Research·2014
Same author

Bayesian Transformation Models for Multivariate Survival Data.

Scandinavian journal of statistics, theory and applications·2014
Same author

Donor killer cell Ig-like receptor B haplotypes, recipient HLA-C1, and HLA-C mismatch enhance the clinical benefit of unrelated transplantation for acute myelogenous leukemia.

Journal of immunology (Baltimore, Md. : 1950)·2014
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

Additive models offer a more consistent approach to analyzing competing risks in cancer studies, such as relapse and death in remission, by properly partitioning covariate effects on treatment failure.

Area of Science:

  • Biostatistics
  • Cancer Research
  • Clinical Trials

Background:

  • Competing risks, including relapse and death in remission, are common in cancer studies and are primary reasons for treatment failure.
  • Current analyses often use distinct proportional hazards models for these outcomes, which can lead to internal inconsistencies.

Purpose of the Study:

  • To propose and illustrate the use of additive models for analyzing competing risks in cancer studies.
  • To demonstrate how additive models provide a more natural and consistent way to partition the effect of covariates on treatment failure.

Main Methods:

  • Comparison of distinct proportional hazards regression models with additive models for hazard rates or cumulative incidence functions.
  • Application of additive models to data from a hematopoietic stem cell transplantation study.

Related Experiment Videos

Main Results:

  • Distinct proportional hazards models can lead to internal inconsistencies when analyzing competing risks.
  • Additive models for hazard rates or cumulative incidence functions offer a more natural approach.
  • Additive models properly partition the effect of covariates on treatment failure into component parts.

Conclusions:

  • Additive models are recommended for analyzing competing risks in cancer studies.
  • These models provide a more consistent and interpretable framework for understanding treatment failure.
  • The study highlights the utility of additive models in hematopoietic stem cell transplantation research.