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Updated: Jun 22, 2026
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Model checks for Cox-type regression models based on optimally weighted martingale residuals.
1Department of Mathematics, Imperial College London, London, UK. a.gandy@imperial.ac.uk
Lifetime Data Analysis
|June 10, 2009
Summary
We developed new directed goodness-of-fit tests for Cox-type regression models in survival analysis. These tests allow researchers to specifically target alternative models, enhancing statistical power in survival data analysis.
Area of Science:
- Biostatistics
- Survival Analysis
- Statistical Modeling
Background:
- Cox-type regression models are widely used in survival analysis.
- Assessing the fit of these models to data is crucial for reliable inference.
- Existing goodness-of-fit tests may lack power against specific alternative hypotheses.
Purpose of the Study:
- To introduce novel directed goodness-of-fit tests for Cox-type regression models.
- To develop tests that are powerful against pre-specified alternative models.
- To provide a flexible framework for model validation in survival analysis.
Main Methods:
- The proposed tests are based on sums of weighted martingale residuals.
- Asymptotic distributions of the test statistics are derived.
- Optimal tests are constructed against specific competing Cox-type models (e.g., different covariates or link functions).
Main Results:
- Simulation studies demonstrate the performance of the directed tests.
- The tests show improved power against targeted alternatives compared to standard tests.
- The methodology was successfully applied to a real-world dataset.
Conclusions:
- Directed goodness-of-fit tests offer a powerful tool for validating Cox-type regression models.
- The choice of alternative hypothesis can significantly enhance test performance.
- This approach improves the reliability of survival analysis results.
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