Network meta-analysis of prediction models using aggregate or individual participant data — a scoping review and recommendations for reporting and conduct

Prediction models are tools used in medicine to estimate a patient's risk of developing a disease or experiencing a health outcome. Many different prediction models exist for the same condition, and it can be difficult for doctors and researchers to know which model performs best. One way to compare multiple models is through a statistical method called network meta-analysis (NMA), which is commonly used to compare treatments but has rarely been applied to prediction models. In our study, we reviewed all published NMAs that evaluated prediction models to see how they were conducted and reported. We looked at whether the studies reported important performance measures, such as how well the models could distinguish between patients with and without the outcome (discrimination), and how well the predictions matched actual outcomes (calibration). We also checked if key NMA assumptions were considered and how analyses were conducted. We found that most studies used summary data instead of patient-level data. Many did not report crucial performance measures and rarely checked NMA assumptions. There was also limited transparency in how the models were analyzed, making it difficult for others to reproduce the results. Our findings show that while NMA has great potential to help compare prediction models and identify the most reliable ones, current practice often lacks the detailed reporting needed to make these comparisons fully trustworthy. We recommend better reporting, sharing of data and analysis code, and careful checking of assumptions to help researchers and doctors choose the most reliable models, ultimately improving patient care.