Long‐term effect of first‐line injectable multiple sclerosis treatments: Input of a time‐dependent propensity score

Abstract

Purpose: The long-term effect of beta-interferon and glatiramer acetate on multiple sclerosis (MS) disability progression has resulted in controversial results, probably due to a lack of appropriate control of biases as raised in observational studies. In particular, the time of the therapeutic decision is difficult to define when the controls are not treated. Methods: This retrospective observational study was based on a series of patients from the MS expert center in Rennes, France. We used a time-dependent propensity score defined as the linear predictor of a Cox model estimating the hazard of being treated at each time from MS onset. The matching procedure resulted in two groups: patients matched as treated and as not yet treated. The restricted mean times (RMST) to reach a moderate level of disability or worsening of the disability were compared between the two groups in an intention-to-treat analysis. Results: Of the 2383 patients included in the study, 556 were matched as treated. The matching procedure provided a good balance of both the time-fixed and the time-dependent covariates. A slight difference was observed for the time to reach a moderate level of disability, in favor of the "not yet treated" group (difference in the RMST: −0.62 [−0.91; −0.33]) while no difference was found in terms of worsening of the disability (−0.03 [−0.24; 0.33]). Conclusion: This unexpected result is probably due to unmeasured confounders. However, this time-dependent PS warrants consideration in long-term effectiveness studies.

High-Efficacy Therapy Discontinuation vs Continuation in Patients 50 Years and Older With Nonactive MS

“…We used a dynamic propensity score (PS), as proposed by Lu and recently used in MS, to match the patients who stopped treatment with patients who had the same probability of stopping treatment at the same time but who had not yet stopped. The first step consisted of modeling the time from inclusion in the study to treatment discontinuation using a cause-specific updated Cox model.…”

Analyzing Interval-Censored Recurrence Event Data with Adjusting Informative Observation Times by Propensity Scores

“…The propensity score represents the likelihood that an individual will receive a specific treatment or intervention, conditional on a set of covariates, and it is a crucial tool for controlling for confounding variables in observational research. The utilization of the propensity score is prevalent in the medical industry [13][14][15][16][17]. Inverse probability of treatment weighting (IPTW) is one of the most important methods to use the propensity score to deal with confounding bias.…”