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      Discontinuation of tofacitinib and TNF inhibitors in patients with rheumatoid arthritis: analysis of pooled data from two registries in Canada

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          Abstract

          Objectives

          The similarity in retention of tumour necrosis factor inhibitors (TNFi) and tofacitinib (TOFA) was previously reported separately by the Ontario Best Practices Research Initiative and the Quebec cohort Rhumadata. However, because of small sample sizes in each registry, we aimed to confirm the findings by repeating the analysis of discontinuation of TNFi compared with TOFA, using pooled data from both these registries.

          Design

          Retrospective cohort study.

          Setting

          Pooled data from two rheumatoid arthritis (RA) registries in Canada.

          Participants

          Patients with RA starting TOFA or TNFi between June 2014 and December 2019 were included. A total of 1318 patients were included TNFi (n=825) or TOFA (n=493).

          Outcome measures

          Time to discontinuation was assessed using Kaplan-Meier survival and Cox proportional hazards regression analysis. Propensity score (PS) stratification (deciles) and PS weighting were used to estimate treatment effects.

          Results

          The mean disease duration in the TNFi group was shorter (8.9 years vs 13 years, p<0.001). Prior biological use (33.9% vs 66.9%, p<0.001) and clinical disease activity index (20.0 vs 22.1, p=0.02) were lower in the TNFi group.

          Discontinuation was reported in 309 (37.5%) and 181 (36.7%) TNFi and TOFA patients, respectively. After covariate adjustment using PS, there was no statistically significant difference between the two groups in discontinuation due to any reason HR=0.96 (95% CI 0.78 to 1.19, p=0.74)) as well as discontinuation due to ineffectiveness only HR=1.08 (95% CI 0.81 to 1.43, p=0.61)).

          TNFi users were less likely to discontinue due to adverse events (AEs) (adjusted HRs: 0.46, 95% CI 0.29 to 0.74; p=0.001). Results remained consistent for firstline users.

          Conclusions

          In this pooled real-world data study, the discontinuation rates overall were similar. However, discontinuation due to AEs was higher in TOFA compared with TNFi users.

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          Most cited references40

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          Multiple Imputation for Nonresponse in Surveys

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            Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

            The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalences, higher‐order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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              Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group

              In observational studies, investigators have no control over the treatment assignment. The treated and non-treated (that is, control) groups may have large differences on their observed covariates, and these differences can lead to biased estimates of treatment effects. Even traditional covariance analysis adjustments may be inadequate to eliminate this bias. The propensity score, defined as the conditional probability of being treated given the covariates, can be used to balance the covariates in the two groups, and therefore reduce this bias. In order to estimate the propensity score, one must model the distribution of the treatment indicator variable given the observed covariates. Once estimated the propensity score can be used to reduce bias through matching, stratification (subclassification), regression adjustment, or some combination of all three. In this tutorial we discuss the uses of propensity score methods for bias reduction, give references to the literature and illustrate the uses through applied examples.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2023
                6 March 2023
                : 13
                : 3
                : e063198
                Affiliations
                [1 ]departmentToronto General Hospital Research Institute , University Health Network , Toronto, Ontario, Canada
                [2 ]IHMPE, Univeristy of Toronto , Toronto, Ontario, Canada
                [3 ]departmentDepartment of Rheumatology , Institut de Rhumatologie de Montréal , Montreal, Québec, Canada
                [4 ]departmentDepartment of Medicine , University of Toronto , Toronto, Ontario, Canada
                Author notes
                [Correspondence to ] Dr Mohammad Movahedi; mmovahed@ 123456uhnresearch.ca
                Author information
                http://orcid.org/0000-0001-6154-0121
                Article
                bmjopen-2022-063198
                10.1136/bmjopen-2022-063198
                9990670
                36878650
                0f33c093-c92e-4605-bd72-0e1b14c5d292
                © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 30 March 2022
                : 05 February 2023
                Categories
                Rheumatology
                1506
                1732
                Original research
                Custom metadata
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                Medicine
                rheumatology,therapeutics
                Medicine
                rheumatology, therapeutics

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