Individual-level surrogacy of MRI lesions for disease severity in RRMS: Methods to quantify predictive power and their application to longitudinal data from recent trials

Background: Individual-level surrogacy (ILS) assesses how well a surrogate endpoint predicts treatment effect at the individual level. The paper discusses mutual information (MI) and the likelihood reduction factor (LRF) as ways to quantify ILS. It also reassesses ILS statements for people with relapsing-remitting multiple sclerosis (RRMS) by using T2 MRI lesions (T2L) as surrogates for disability and disease activity. ILS is often reported using inadequate concepts, e.g. subgroup analysis, correlation, odds ratios, sensitivity/specificity, or metrics based on Prentice’s criteria. Methods: LRF assesses ILS quality by determining prediction quality through shared information (MI) between surrogate(s) and clinical endpoint(s). A simulation study validates LRF as a measure of ILS quality. Individual-level data from ten randomized controlled trials (n = 5673) provided longitudinal information on T2L, T2 MRI lesion volumes (T2V), future disability progression (EDSS), and relapses. LRFs for different scenarios were calculated. Results were compared to those obtained by methods commonly applied in RRMS literature. Results: Simulations confirmed the robustness of LRF as a reliable ILS quality measure. Two of ten trials showed weak ILS between T2V and EDSS (LRF = 0.21, CI95%: 0.16–0.26; LRF = 0.28, CI95%: 0.23–0.34). Other LRFs were below 0.2. A method commonly used in the MS literature also showed no strong ILS. Conclusion: LRF is an important measure to quantify ILS and prediction quality. But it is rarely applied in RRMS research. LRF did not reveal robust surrogacy patterns when applied to data of ten clinical trials. Existing surrogacy claims should be reassessed since ILS assessments in the MS literature may have major limitations.