Dealing With Treatment-Confounder Feedback and Sparse Follow-up in Longitudinal Studies: Application of a Marginal Structural Model in a Multiple Sclerosis Cohort. Issue 5 (30th October 2020)

• Record Type: Journal Article
• Authors: Karim, Mohammad Ehsanul; Tremlett, Helen; Zhu, Feng; Petkau, John; Kingwell, Elaine
• Is Part Of: American journal of epidemiology. Volume 190:Issue 5(2021)
• Page Start: 908
• Page End: 917
• Publication Date: 2020-10-30
• Subjects: bias -- confounding factors -- epidemiologic methods -- inverse probability weighting -- marginal structural model -- multiple imputation -- multiple sclerosis; Epidemiology -- Periodicals; Public health -- Periodicals; 614.4
• DOI: 10.1093/aje/kwaa243
• Languages: English
• ISSNs: 0002-9262

Abstract

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 multilevel 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.