Assessing heterogeneity of treatment effect in multiple sclerosis trials

Multiple sclerosis (MS) is heterogeneous with respect to outcomes, and evaluating possible heterogeneity of treatment effect (HTE) is of high interest. HTE is non-random variation in the magnitude of a treatment effect on a clinical outcome across levels of a covariate (i.e. a patient attribute or set of attributes). Multiple statistical techniques can evaluate HTE. The simplest but most bias-prone is conventional one variable-at-a-time subgroup analysis. Recently, multivariable predictive approaches have been promoted to provide more patient-centered results, by accounting for multiple relevant attributes simultaneously. We review approaches used to estimate HTE in clinical trials of MS.

Automated summary

Multiple sclerosis (MS) has varied outcomes, and evaluating heterogeneity of treatment effect (HTE) is important. Various statistical techniques can assess HTE, with multivariable predictive approaches offering more patient-centered results. This review examines methods used to estimate HTE in MS clinical trials.