Related Experiment Video
Updated: Jun 30, 2026
10:46
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Correlation Is Not Prediction: Reassessing Predictive MRI Evidence in Guidelines for Persons With Relapsing-Remitting
Dulat Minas1,2, Stefan Buchka1,2, Joachim Havla3
1Department of Medical Information Processing, Biometry, and Epidemiology, Medical Faculty, Ludwig-Maximilian University Munich, Munich, Germany.
Journal of Central Nervous System Disease
|June 29, 2026
Summary
Current relapsing-remitting multiple sclerosis (RRMS) guidelines often cite MRI outcomes for prediction, but evidence quality is uncertain. Future guidelines need validated, individualized risk predictions for accurate treatment decisions.
Area of Science:
- Neurology
- Medical Imaging
- Biostatistics
Background:
- Accurate prediction of disease course in relapsing-remitting multiple sclerosis (RRMS) is crucial for effective treatment monitoring and decision-making.
- Magnetic Resonance Imaging (MRI) outcomes are frequently cited in major MS guidelines (MAGNIMS, CMSWG) as predictive, yet the methodological rigor of this evidence is questionable.
Purpose of the Study:
- To critically evaluate the methodological standards of predictive claims regarding MRI outcomes within four key multiple sclerosis (MS) guidelines.
- To assess the quality of evidence supporting MRI-based predictions in RRMS treatment guidelines.
Main Methods:
- A content review of citations within the MAGNIMS (2015, 2021) and CMSWG (2013, 2020) guideline publications was performed.
- Evaluated sources for quantitative predictive evidence, including predictive values with confidence intervals, Kaplan-Meier estimates, or validated prediction models (assessing calibration and discrimination).
- Examined the use of statistical measures such as correlations, odds ratios, hazard ratios, Prentice criteria, and likelihood ratio tests.
Main Results:
- Most predictive statements in the guidelines relied on secondary citations and association-based measures (e.g., odds ratios, hazard ratios, correlations).
- While some studies reported predictive values, confidence intervals were often missing. Validated prediction models were rarely cited, with only one undergoing full external validation.
- Advanced statistical methods for prediction accuracy were largely absent in the cited evidence.
Conclusions:
- Guideline statements on MRI prediction in RRMS currently emphasize associations over validated, individualized risk predictions.
- The evidence lacks quantification of individual risk and robust assessment of accuracy, calibration, discrimination, or robustness.
- Future guidelines should mandate prospective risk estimates with confidence intervals, externally validated models, and evaluation of clinical utility for trustworthy evidence.
