Abstract
Introduction
The development of an algorithm that provides accurate individualised estimates of revision risk could help patients make informed surgical treatment choices. This requires building a survival model based on fixed and modifiable risk factors that predict outcome at the individual level. Here we compare different survival models for predicting prosthesis survivorship after hip replacement for osteoarthritis using data from the National Joint Registry for England, Wales, Northern Ireland and the Isle of Man (NJR).
Methods
In this comparative study we implemented parametric and flexible parametric (FP) methods and random survival forests (RSF). The overall performance of the parametric models was compared using Akaike information criterion (AIC). The preferred parametric model and the RSF algorithm were further compared in terms of the Brier score, concordance index and calibration via repeated five-fold cross-validation.
Results
The dataset contains 327 238 hip replacements for osteoarthritis carried out in England and Wales between 2003 and 2015. The AIC value for the FP model was the lowest. The averages of survival probability estimates are shown in Figure 1. The integrated Brier score of the FP model and the RSF approach over 10 years were similar: 0.011 (95% confidence interval: 0.011–0.011). The concordance index of the FP model at 10 years was 59.4% (95% confidence interval: 59.4%–59.4%). The concordance index was 56.2% (56.1%–56.3%) for the RSF method. Calibration plots for the FP model and the RSF algorithm are presented in Figure 2.
Conclusion
The flexible parametric model outperformed other commonly used survival models across chosen validation criteria. However, it does not provide high discriminatory power at the individual level. This might be due to variable outcomes generated by different fixation or bearing types that cannot be captured using a single model. More complex multi-state models may provide better discrimination.
For any figures and tables, please contact the authors directly.
