Table 3.

NO.MS: strengths and limitations of the data set for future analyses.

Strengths	Limitations
1. Data set  - Rich data set from prospectively acquired clinical and imaging trials  - All MS phenotypes and POMS included  - High quality assessments and data (study protocols, harmonised assessments and data curation)  - Broad age- and disability ranges  - Placebo data (all phenotypes)  - Randomised-controlled trials as well as observational trials  - Standardised assessments of relapses and disability (EDSS, including functional scores) by trained physicians  - Definitions of outcomes relatively standardised or differences understood (since all trials conducted by a single sponsor), enabling data harmonisation or selection for analysis  - MRI scans (defaced) available in NIFTI format for unified image analyses  - Additional valuable data on measures such as cognition, PROs and biomarker	1. Data set  - Selection bias: Patients represent selected populations based on the eligibility criteria of study protocols and may be non-representative of routine clinical practice (including selective DMT use)  - Studies conducted by single sponsor  - Limited biological and genetic characterisation  - Study populations may change over time (e.g. to less activity)
2. Follow-up duration  - Long (up to 15 years) follow-up  - Patient-level longitudinal high quality clinical data, including regular standardised neurological assessments  - Includes RRMS patients who transitioned to SPMS while on trial, allowing to study the onset of progressive disease  - Patient-level longitudinal MRI scans (defaced) available in NIFTI format to support re-analysis of MRI scans and linkable to the de-identified clinical data	2. Follow-up duration  - Variable longitudinal follow-up  - Informative censoring is a possibility in some cases  - Limited follow-up in PMS cohorts (additional long-term data are being collected in SPMS)
3. Data analysis  - Longitudinal, harmonised, robust and scalable voxel-wise analysis of MRI scans across studies is ongoing to extract new features  - Applicable for advanced analytical approaches including supervised and unsupervised machine learning on top of conventional approaches	3. Data analysis  - Challenging as MRI scans are heterogeneous from multicentre trials over almost 20 years (scanner/software, sites and resolution)

DMT: disease modifying therapy; EDSS: expanded disability status scale; MRI: magnetic resonance imaging; MS: multiple sclerosis; PMS: progressive MS; POMS: paediatric-onset MS; PRO: patient-reported outcomes; SPMS: secondary progressive MS.