| Description: |
Background Prediction models are essential in clinical decision-making for estimating the probability of current (diagnosis, screening) or future (prognosis) outcomes. Network meta-analysis (NMA) serves as a powerful tool to compare the performance of multiple prediction models simultaneously. However, there is hardly any guidance on methods and reporting for studies employing NMA to evaluate prediction models. Objective To provide an overview of NMAs assessing prediction model (external validation) performance, regardless of whether they use aggregate data (AD) or individual participant data (IPD). In addition, we offer recommendations for improving the reporting and conduct of NMAs in prediction model research. Methods We searched PubMed and Embase up to September 1, 2025, to identify studies that addressed the evaluation of diagnostic or prognostic prediction model performance using NMA. We included articles that employed NMA to compare and assess at least three prediction models. We summarized the identified studies based on, eg, their application (diagnostic vs prognostic), data use (AD vs IPD), medical contexts in which the models were assessed, and evaluation metrics applied (eg, discrimination, calibration, and (re)classification). In addition, we examined the statistical approaches employed, the NMA assumptions (such as consistency, transitivity, and exchangeability), and the ranking methods used for model comparison. Results After screening 2436 articles, 28 were included. Twenty-six studies (92.9%) used AD, while two (7.1%) used IPD. Hospital care was the most common setting (n = 22; 78.6%), with respirology (n = 7; 25.0%) and cardiology (n = 5; 17.9%) as the most frequently studied clinical domains. Key NMA assumptions were addressed differently across the 28 NMAs: 14.3% (n = 4) discussed transitivity, similarity, or exchangeability, and 53.6% (n = 15) tested for consistency. The statistical approach also varied, with 60.7% of studies (n = 17) reporting Bayesian methods and 17.9% (n = 5) reporting ... |