Related Experiment Video
Updated: Apr 11, 2026
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
11.1K
The proportional odds cumulative incidence model for competing risks
Frank Eriksson1, Jianing Li2, Thomas Scheike1
1Section of Biostatistics, University of Copenhagen, Øster Farimagsgade 5, Copenhagen DK-1014, Denmark.
Biometrics
|May 28, 2015
Summary
We present a new estimator for the proportional odds cumulative incidence model, improving upon existing methods for competing risks data. This approach offers a practical and interpretable odds ratio for regression parameters, enhancing clinical research applications.
Area of Science:
- Biostatistics
- Survival Analysis
- Medical Statistics
Background:
- Competing risks data present challenges in survival analysis.
- The proportional odds cumulative incidence model offers an interpretable odds ratio but lacks robust estimation methods.
Purpose of the Study:
- To propose a novel and reliable estimation procedure for the proportional odds cumulative incidence model.
- To introduce a goodness-of-fit test for the proportional odds assumption in competing risks data.
Main Methods:
- Development of a new estimator for the proportional odds cumulative incidence model.
- Derivation of large sample properties and asymptotic variance estimators.
- Assessment of finite-sample properties through simulations.
Main Results:
- The proposed estimation procedure significantly outperforms existing methods.
- The new goodness-of-fit test effectively assesses the proportional odds assumption.
- The method demonstrates practical utility in a bone marrow transplant study.
Conclusions:
- The developed estimator provides a reliable and interpretable tool for analyzing competing risks data.
- This work addresses a practical gap in the application of the proportional odds cumulative incidence model.
- The findings are validated through simulations and a real-world case study.
Related Concept Videos
01:12Relative Risk
2.6K
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...
2.6K
01:11Hazard Rate
514
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
514
01:09Odds Ratio
2.3K
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
2.3K
01:12Hazard Ratio
731
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...
For example, in a clinical trial...
731
01:14Parametric Survival Analysis: Weibull and Exponential Methods
1.3K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.3K
01:15Assumptions of Survival Analysis
493
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
493