Dealing with Treatment-confounder Feedback and Sparse Follow-up in Longitudinal studies - Application of a Marginal Structural Model in a Multiple Sclerosis Cohort
Abstract The beta-interferons are widely prescribed platform therapies for patients with multiple sclerosis (MS). We accessed a cohort of patients with relapsing onset MS from British Columbia, Canada (1995-2013) to examine the potential survival advantage associated with beta-interferon exposure using a marginal structural model. Accounting for potential treatment-confounder feedback between comorbidity, MS disease progression and beta-interferon exposure, we found an association between beta-interferon exposure of at least 6 contiguous months and improved survival (hazard ratio (HR) = 0.63, 95% confidence interval 0.47-0.86). We also assessed potential effect modifications by sex, baseline age or baseline disease duration, and found these factors to be important effect modifiers. Sparse follow-up due to variability in patient contact with the health system is one of the biggest challenges in longitudinal analyses. We considered several single-level and multi-level multiple imputation approaches to deal with sparse follow-up and disease progression information; both types of approach produced similar estimates. Compared to ad hoc imputation approaches, such as linear interpolation (HR: 0.63), and last observation carried forward (HR: 0.65), all multiple imputation approaches produced a smaller hazard ratio (HR: 0.53), although the direction of effect and conclusions drawn concerning the survival advantage remained the same.