The Network Relative Model Accuracy (NeRMA) Score can quantify the relative accuracy of prediction models in concurrent external validations

Background Network meta‐analysis (NMA) quantifies the relative efficacy of three or more interventions from trials evaluating some, but usually not all, treatments. This study applied the analytical approach of NMA to quantify the relative accuracy of prediction models with distinct patient applicab...

Full description

Saved in:
Bibliographic Details
Published in:Journal of evaluation in clinical practice 2023-03, Vol.29 (2), p.351-358
Main Authors: Walraven, Carl, Tuna, Meltem
Format: Article
Language:English
Subjects:
Accuracy
health services research
Humans
medical research
Models, Statistical
Network Meta-Analysis
systematic reviews
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Background Network meta‐analysis (NMA) quantifies the relative efficacy of three or more interventions from trials evaluating some, but usually not all, treatments. This study applied the analytical approach of NMA to quantify the relative accuracy of prediction models with distinct patient applicability that are evaluated on the same population (‘concurrent external validation’). Methods We simulated binary events in 5000 patients using a known risk function. We biased the risk function and modified its precision by pre‐specified amounts to create 15 prediction models with varying accuracy and distinct patient applicability. Prediction model accuracy was measured using the Scaled Brier Score (SBS). Overall prediction model accuracy was measured using fixed‐effects methods accounting for distinct model applicability patterns. Prediction model accuracy was summarized as the Network Relative Model Accuracy (NeRMA) Score which increases as models become more accurate and ranges from
ISSN:1356-1294
1365-2753
DOI:10.1111/jep.13779