The Experts below are selected from a list of 39840 Experts worldwide ranked by ideXlab platform

Elizabeth A Ashley - One of the best experts on this subject based on the ideXlab platform.

Prabin Dahal - One of the best experts on this subject based on the ideXlab platform.

Antoine Pariente - One of the best experts on this subject based on the ideXlab platform.

  • Comparison of Treatment Persistence with Dabigatran or Rivaroxaban versus Vitamin K Antagonist Oral Anticoagulants in Atrial Fibrillation Patients: A Competing Risk Analysis in the French National Health Care Databases
    Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy, 2018
    Co-Authors: Géric Maura, C. Billionnet, Francois Alla, J. J. Gagne, Antoine Pariente
    Abstract:

    BACKGROUND: Direct oral anticoagulants (DOACs) have been proposed as a more convenient alternative to vitamin K antagonists (VKAs), which are commonly associated with poor treatment persistence in nonvalvular atrial fibrillation (nv-AF). METHODS: Using data from the French National Healthcare databases (Regime General, 50 million beneficiaries), a cohort study was conducted to compare the 1-year non-persistence rates in nv-AF patients initiating dabigatran (N=11,141) or rivaroxaban (N=11,126) versus VKA (N=11,998). Treatment discontinuation was defined as a switch between oral anticoagulant (OAC) classes or a 60-day gap with no medication coverage, with the additional criterion of no reimbursement for international normalized ratio monitoring during this gap for VKA patients. Considering death as a Competing Risk, differences between 1-year discontinuation rates were used to compare each DOAC versus VKA. 95% confidence intervals [CIs] were estimated via bootstrapping. Baseline patient characteristics were adjusted using inverse probability of treatment weighting. Subgroup analyses considered DOAC dose at initiation, age, Risk of stroke, and bleeding. RESULTS: Adjusted 1-year discontinuation rates were higher for dabigatran than for VKA new users (36.8% vs 30.2%; difference: 6.6% [95% CI, 5.5 to 7.6]) and for rivaroxaban versus VKA new users (33.4% vs 30.4%; 3.0% [1.9 to 4.1]). Similar differences were found in all subgroup analyses, except in dabigatran and rivaroxaban patients

  • comparison of treatment persistence with dabigatran or rivaroxaban versus vitamin k antagonist oral anticoagulants in atrial fibrillation patients a Competing Risk analysis in the french national health care databases
    Pharmacotherapy, 2018
    Co-Authors: Géric Maura, C. Billionnet, Francois Alla, J. J. Gagne, Antoine Pariente
    Abstract:

    Background Direct oral anticoagulants (DOACs) have been proposed as a more convenient alternative to vitamin K antagonists (VKAs), which are commonly associated with poor treatment persistence in nonvalvular atrial fibrillation (nv-AF). Methods Using data from the French National Healthcare databases (Regime General, 50 million beneficiaries), a cohort study was conducted to compare the 1-year non-persistence rates in nv-AF patients initiating dabigatran (N=11,141) or rivaroxaban (N=11,126) versus VKA (N=11,998). Treatment discontinuation was defined as a switch between oral anticoagulant (OAC) classes or a 60-day gap with no medication coverage, with the additional criterion of no reimbursement for international normalized ratio monitoring during this gap for VKA patients. Considering death as a Competing Risk, differences between 1-year discontinuation rates were used to compare each DOAC versus VKA. 95% confidence intervals [CIs] were estimated via bootstrapping. Baseline patient characteristics were adjusted using inverse probability of treatment weighting. Subgroup analyses considered DOAC dose at initiation, age, Risk of stroke, and bleeding. Results Adjusted 1-year discontinuation rates were higher for dabigatran than for VKA new users (36.8% vs 30.2%; difference: 6.6% [95% CI, 5.5 to 7.6]) and for rivaroxaban versus VKA new users (33.4% vs 30.4%; 3.0% [1.9 to 4.1]). Similar differences were found in all subgroup analyses, except in dabigatran and rivaroxaban patients <75 y (dabigatran vs VKA: 0.3% [-1.4 to 1.8]; rivaroxaban vs VKA: -2.6% [-4.3 to -0.9]) and dabigatran 150 mg new users (-1.1% [-3.1 to 0.7]). Consistent results were obtained when considering both switches between OAC classes and death as Competing Risks of treatment discontinuation. Conclusion Results from this nationwide cohort study showed high non-persistence levels with all OACs and suggest that persistence with both dabigatran and rivaroxaban therapy is not better than persistence with VKA therapy. Hospitalizations for bleeding among non-persistent patients were unlikely to explain these high non-persistence rates. This article is protected by copyright. All rights reserved.

Bertram L Kasiske - One of the best experts on this subject based on the ideXlab platform.

Meijie Zhang - One of the best experts on this subject based on the ideXlab platform.

  • instrumental variable with Competing Risk model
    Statistics in Medicine, 2017
    Co-Authors: Cheng Zheng, Ran Dai, Parameswaran Hari, Meijie Zhang
    Abstract:

    In this paper, we discuss causal inference on the efficacy of a treatment or medication on a time-to-event outcome with Competing Risks. Although the treatment group can be randomized, there can be confoundings between the compliance and the outcome. Unmeasured confoundings may exist even after adjustment for measured covariates. Instrumental variable methods are commonly used to yield consistent estimations of causal parameters in the presence of unmeasured confoundings. On the basis of a semiparametric additive hazard model for the subdistribution hazard, we propose an instrumental variable estimator to yield consistent estimation of efficacy in the presence of unmeasured confoundings for Competing Risk settings. We derived the asymptotic properties for the proposed estimator. The estimator is shown to be well performed under finite sample size according to simulation results. We applied our method to a real transplant data example and showed that the unmeasured confoundings lead to significant bias in the estimation of the effect (about 50% attenuated). Copyright © 2017 John Wiley & Sons, Ltd.

  • instrumental variable with Competing Risk model
    arXiv: Methodology, 2016
    Co-Authors: Cheng Zheng, Ran Dai, Parameswaran Hari, Meijie Zhang
    Abstract:

    In this paper, we discuss causal inference on the efficacy of a treatment or medication on a time-to-event outcome with Competing Risks. Although the treatment group can be randomized, there can be confoundings between the compliance and the outcome. Unmeasured confoundings may exist even after adjustment for measured co- variates. Instrumental variable (IV) methods are commonly used to yield consistent estimations of causal parameters in the presence of unmeasured confoundings. Based on a semi-parametric additive hazard model for the subdistribution hazard, we pro- pose an instrumental variable estimator to yield consistent estimation of efficacy in the presence of unmeasured confoundings for Competing Risk settings. We derived the asymptotic properties for the proposed estimator. The estimator is shown to be well per- formed under finite sample size according to simulation results. We applied our method to a real transplant data example and showed that the unmeasured confoundings lead to significant bias in the estimation of the effect (about 50% attenuated).

  • analyzing Competing Risk data using the r timereg package
    Journal of Statistical Software, 2011
    Co-Authors: Thomas H Scheike, Meijie Zhang
    Abstract:

    In this paper we describe flexible Competing Risks regression models using the comp.Risk() function available in the timereg package for R based on Scheike et al. (2008). Regression models are specified for the transition probabilities, that is the cumulative incidence in the Competing Risks setting. The model contains the Fine and Gray (1999) model as a special case. This can be used to do goodness-of-fit test for the subdistribution hazards’ proportionality assumption (Scheike and Zhang 2008). The program can also construct confidence bands for predicted cumulative incidence curves.We apply the methods to data on follicular cell lymphoma from Pintilie (2007), where the Competing Risks are disease relapse and death without relapse. There is important non-proportionality present in the data, and it is demonstrated how one can analyze these data using the flexible regression models.