Dealing with Treatment-Confounder Feedback and Sparse Follow-Up in Longitudinal Studies: Application of a Marginal Structural Model in a Multiple Sclerosis Cohort

• Activity Number: 160 - Novel Perspectives on the Estimation of Longitudinal Effects
• Type: Topic Contributed
• Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 11:50 AM
• Sponsor: SSC (Statistical Society of Canada)
• Author(s): Mohammad Ehsanul Karim* and Helen Tremlett and Feng Zhu and John Petkau and Elaine Kingwell
• Companies: University of British Columbia and University of British Columbia and University of British Columbia and University of British Columbia and University of British Columbia
• Keywords: causal inference; multiple sclerosis; longitudinal analysis; marginal structural model; survival analysis; multiple imputation

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 (HR 0.63, 95% CI 0.47-0.86). We assessed potential effect modifications. 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. We compared ad hoc imputation approaches, such as linear interpolation, and last observation carried forward, with multiple imputation approaches, and conclusions drawn concerning the survival advantage remained the same.