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      First‐line antiepileptic drug treatment in glioma patients with epilepsy: Levetiracetam vs valproic acid

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          Abstract

          Objective

          This study aimed at estimating the cumulative incidence of antiepileptic drug (AED) treatment failure of first‐line monotherapy levetiracetam vs valproic acid in glioma patients with epilepsy.

          Methods

          In this retrospective observational study, a competing risks model was used to estimate the cumulative incidence of treatment failure, from AED treatment initiation, for the two AEDs with death as a competing event. Patients were matched on baseline covariates potentially related to treatment assignment and outcomes of interest according to the nearest neighbor propensity score matching technique. Maximum duration of follow‐up was 36 months.

          Results

          In total, 776 patients using levetiracetam and 659 using valproic acid were identified. Matching resulted in two equal groups of 429 patients, with similar covariate distribution. The cumulative incidence of treatment failure for any reason was significantly lower for levetiracetam compared to valproic acid (12 months: 33% [95% confidence interval (CI) 29%–38%] vs 50% [95% CI 45%–55%]; P < .001). When looking at specific reasons of treatment failure, treatment failure due to uncontrolled seizures was significantly lower for levetiracetam compared to valproic acid (12 months: 16% [95% CI 12%–19%] vs 28% [95% CI 23%–32%]; P < 0.001), but no differences were found for treatment failure due to adverse effects (12 months: 14% [95% CI 11%–18%] vs 15% [95% CI 11%–18%]; P = .636).

          Significance

          Our results suggest that levetiracetam may have favorable efficacy compared to valproic acid, whereas level of toxicity seems similar. Therefore, levetiracetam seems to be the preferred choice for first‐line AED treatment in patients with glioma.

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          Most cited references41

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          The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary.

          The 2016 World Health Organization Classification of Tumors of the Central Nervous System is both a conceptual and practical advance over its 2007 predecessor. For the first time, the WHO classification of CNS tumors uses molecular parameters in addition to histology to define many tumor entities, thus formulating a concept for how CNS tumor diagnoses should be structured in the molecular era. As such, the 2016 CNS WHO presents major restructuring of the diffuse gliomas, medulloblastomas and other embryonal tumors, and incorporates new entities that are defined by both histology and molecular features, including glioblastoma, IDH-wildtype and glioblastoma, IDH-mutant; diffuse midline glioma, H3 K27M-mutant; RELA fusion-positive ependymoma; medulloblastoma, WNT-activated and medulloblastoma, SHH-activated; and embryonal tumour with multilayered rosettes, C19MC-altered. The 2016 edition has added newly recognized neoplasms, and has deleted some entities, variants and patterns that no longer have diagnostic and/or biological relevance. Other notable changes include the addition of brain invasion as a criterion for atypical meningioma and the introduction of a soft tissue-type grading system for the now combined entity of solitary fibrous tumor / hemangiopericytoma-a departure from the manner by which other CNS tumors are graded. Overall, it is hoped that the 2016 CNS WHO will facilitate clinical, experimental and epidemiological studies that will lead to improvements in the lives of patients with brain tumors.
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            An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

            The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
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              A Class of $K$-Sample Tests for Comparing the Cumulative Incidence of a Competing Risk

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                Author and article information

                Contributors
                pbvandermeer@lumc.nl
                Journal
                Epilepsia
                Epilepsia
                10.1111/(ISSN)1528-1167
                EPI
                Epilepsia
                John Wiley and Sons Inc. (Hoboken )
                0013-9580
                1528-1167
                18 March 2021
                May 2021
                : 62
                : 5 ( doiID: 10.1111/epi.v62.5 )
                : 1119-1129
                Affiliations
                [ 1 ] Department of Neurology Leiden University Medical Center Leiden The Netherlands
                [ 2 ] Department of Neurology Haaglanden Medical Center The Hague The Netherlands
                [ 3 ] Department of Biomedical Data Sciences Medical Statistics Leiden University Medical Center Leiden The Netherlands
                [ 4 ] Mathematical Institute Leiden University Leiden The Netherlands
                [ 5 ] Department of Neurology Amsterdam University Medical Center Amsterdam The Netherlands
                [ 6 ] Department of Neurology Erasmus Medical Center Rotterdam The Netherlands
                Author notes
                [*] [* ] Correspondence

                Pim B. van der Meer, Department of Neurology, Leiden University Medical Center, PO BOX 9600, 2300 RC Leiden, The Netherlands.

                Email: pbvandermeer@ 123456lumc.nl

                Author information
                https://orcid.org/0000-0003-3700-8906
                Article
                EPI16880
                10.1111/epi.16880
                8251728
                33735464
                15cd5b91-3143-43a1-98c9-556b08c890b3
                © 2021 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 04 March 2021
                : 07 November 2020
                : 04 March 2021
                Page count
                Figures: 2, Tables: 2, Pages: 11, Words: 7210
                Categories
                Full‐length Original Research
                Full‐length Original Research
                Custom metadata
                2.0
                May 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.4 mode:remove_FC converted:02.07.2021

                Neurology
                antiepileptic drug,glioma,levetiracetam,seizures,valproic acid
                Neurology
                antiepileptic drug, glioma, levetiracetam, seizures, valproic acid

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