Competing and Noncompeting Risk Models for Predicting Kidney Allograft Failure
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View abstract on PubMed
Summary
This summary is machine-generated.This study found that both competing risk and non-competing risk models similarly predict long-term kidney graft failure. These prognostic models are crucial for clinical trials and patient management in kidney transplantation.
Area Of Science
- Nephrology
- Biostatistics
- Clinical Epidemiology
Background
- Prognostic models are vital for clinical trials and patient management.
- The impact of competing risks on model performance is under-investigated.
- Kidney transplantation involves competing risks: allograft failure and death with a functioning graft.
Purpose Of The Study
- To assess the performance of competing risk and non-competing risk models in kidney transplantation.
- To compare model development, stability, discrimination, calibration, fit, utility, and generalizability.
- To provide an exhaustive assessment using over 15 metrics.
Main Methods
- Included 11,046 kidney transplant recipients from 10 countries.
- Developed prediction models for graft failure using Cox, Fine-Gray, and cause-specific Cox regression.
- Assessed models using a transparent framework for competing and non-competing risk modeling.
Main Results
- 1,497 (14%) graft failures and 1,003 (9%) deaths occurred.
- Both model types showed similar and stable risk estimates for graft failure.
- Discrimination and overall fit were comparable, with concordance indices from 0.76 to 0.87.
Conclusions
- Competing and non-competing risk models demonstrated similar performance in predicting long-term kidney graft failure.
- Models performed well across various subpopulations and clinical scenarios.
- A competing risk approach showed a trend towards improved calibration in some high-risk groups.
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