Abstract
Background
Two key considerations in evaluating survival prediction models are predictability (the ability to accurately predict survival risks) and reproducibility (the ability to generalize predictions across studies or centers). This study explores approaches to assessing the reproducibility of survival risk predictions across medical centers using consistency and transferability metrics.
Methods
Reproducibility was assessed using two public datasets: lung cancer data from four centers and colon cancer data from two centers. Predictability was evaluated using eight survival prediction models, including clinical and gene expression data-based models. Metrics for reproducibility, such as consistency (agreement between centers) and transferability (applicability of models and signatures), were calculated using correlation coefficients.
Results
The analysis demonstrated good consistency and transferability for clinical models, particularly the Cox proportional hazards model. Models combining clinical and gene expression data offered limited improvements. The colon cancer dataset revealed significant variability in gene expression model performance, highlighting challenges in reproducibility across centers.
Conclusions
Models based on clinical variables demonstrated high reproducibility across centers, while gene expression models exhibited limited generalizability. Further studies are needed to enhance reproducibility and address variability in gene expression data.
Key Questions
1. What is the significance of reproducibility in survival prediction models?
Reproducibility ensures that survival prediction models can reliably generalize to data from other centers, enabling broader clinical application.
2. What approaches are used to assess reproducibility?
Consistency and transferability metrics, such as correlation coefficients, are used to evaluate agreement and applicability of predictions across centers.
3. How do clinical and gene expression models compare in reproducibility?
Clinical models, particularly the Cox proportional hazards model, showed better reproducibility than gene expression models, which faced challenges with variability.
4. What are the implications for future research?
Future studies should focus on improving gene expression model reproducibility and addressing variability across datasets to ensure effective clinical integration.
