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Updated: Dec 18, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Comparison of Cox proportional hazards regression and generalized Cox regression models applied in dementia risk
Jantje Goerdten1, Isabelle Carrière2, Graciela Muniz-Terrera1
1Edinburgh Dementia Prevention & Centre for Clinical Brain Sciences University of Edinburgh Edinburgh UK.
Introduction:
The frequently used Cox regression applies two critical assumptions, which might not hold for all predictors. In this study, the results from a Cox regression model (CM) and a generalized Cox regression model (GCM) are compared.
Methods:
Data are from the Survey of Health, Ageing and Retirement in Europe (SHARE), which includes approximately 140,000 individuals aged 50 or older followed over seven waves. CMs and GCMs are used to estimate dementia risk. The results are internally and externally validated.
Results:
None of the predictors included in the analyses fulfilled the assumptions of Cox regression. Both models predict dementia moderately well (10-year risk: 0.737; 95% confidence interval [CI]: 0.699, 0.773; CM and 0.746; 95% CI: 0.710, 0.785; GCM).
Discussion:
The GCM performs significantly better than the CM when comparing pseudo-R2 and the log-likelihood. GCMs enable researcher to test the assumptions used by Cox regression independently and relax these assumptions if necessary.
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