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      Preference for Oral and Injectable GLP-1 RA Therapy Profiles in Japanese Patients with Type 2 Diabetes: A Discrete Choice Experiment

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          Abstract

          Introduction

          Glucagon-like peptide 1 (GLP-1) receptor agonists (RAs) approved to date are administered by injection; therefore, patient perceptions of an oral GLP-1 RA are unknown. This discrete choice experiment explored preferences for (unbranded) oral and injectable GLP-1 RA profiles among Japanese patients with type 2 diabetes (T2D).

          Methods

          An online survey was designed using literature review and qualitative interview findings, and administered to Japanese patients with T2D and HbA 1c ≥ 7.0% receiving oral antiglycaemic medication (with no experience of injectable antiglycaemic medication). Therapy profiles were created using Japanese head-to-head trial data for orally administered semaglutide (7 mg and 14 mg), injectable dulaglutide (0.75 mg), and injectable liraglutide (0.9 mg). Profiles were not labelled. Choice tasks tested preference between hypothetical profiles, preference between profiles with actual trial data, and willingness to initiate treatment. Relative importance of attributes was determined using conditional logit regression.

          Results

          A total of 500 respondents were analysed: mean age 61.2 years; 93.8% male; mean HbA 1c 7.6%; 78.2% with HbA 1c ≥ 7.0 to < 8%; 89% with HbA 1c above personal target. Mean BMI was 25.4 kg/m 2; 49% had obesity (≥ 25 kg/m 2). The treatment attribute with greatest importance was mode and frequency of administration (49.1%), followed by nausea risk (30.8%), weight change (11.3%), and HbA 1c change (8.8%). Oral semaglutide 7 and 14 mg-like profiles were both preferred: the 7 mg-like profile was preferred over dulaglutide (by 91.0% of respondents) and liraglutide (by 89.4%); the 14 mg-like profile was preferred over dulaglutide (by 88.2%) and liraglutide (by 94.4%). Willingness to initiate treatment was also higher for orally administered semaglutide-like profiles: 62.4% with 7 mg and 64.0% with 14 mg, versus 13.6% and 11.0% with injectable GLP-1 RA-like profiles. Subgroup results were generally consistent with the overall sample.

          Conclusion

          Japanese patients with T2D appear to prefer oral GLP-1 RA profiles over injectable GLP-1 RA profiles, and administration appears to be the most important factor in this decision. This highlights the unmet need for an effective and orally administered GLP-1 RA for the treatment of T2D in Japan.

          Electronic Supplementary Material

          The online version of this article (10.1007/s12325-020-01561-1) contains supplementary material, which is available to authorized users.

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          Most cited references 41

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          Brief questions to identify patients with inadequate health literacy.

          No practical method for identifying patients with low heath literacy exists. We sought to develop screening questions for identifying patients with inadequate or marginal health literacy. Patients (n=332) at a VA preoperative clinic completed in-person interviews that included 16 health literacy screening questions on a 5-point Likert scale, followed by a validated health literacy measure, the Short Test of Functional Health Literacy in Adults (STOHFLA). Based on the STOFHLA, patients were classified as having either inadequate, marginal, or adequate health literacy. Each of the 16 screening questions was evaluated and compared to two comparison standards: (1) inadequate health literacy and (2) inadequate or marginal health literacy on the STOHFLA. Fifteen participants (4.5%) had inadequate health literacy and 25 (7.5%) had marginal health literacy on the STOHFLA. Three of the screening questions, "How often do you have someone help you read hospital materials?" "How confident are you filling out medical forms by yourself?" and "How often do you have problems learning about your medical condition because of difficulty understanding written information?" were effective in detecting inadequate health literacy (area under the receiver operating characteristic curve of 0.87, 0.80, and 0.76, respectively). These questions were weaker for identifying patients with marginal health literacy. Three questions were each effective screening tests for inadequate health literacy in this population.
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            2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2018.

              (2017)
            The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
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              Evaluating Random Forests for Survival Analysis using Prediction Error Curves.

              Prediction error curves are increasingly used to assess and compare predictions in survival analysis. This article surveys the R package pec which provides a set of functions for efficient computation of prediction error curves. The software implements inverse probability of censoring weights to deal with right censored data and several variants of cross-validation to deal with the apparent error problem. In principle, all kinds of prediction models can be assessed, and the package readily supports most traditional regression modeling strategies, like Cox regression or additive hazard regression, as well as state of the art machine learning methods such as random forests, a nonparametric method which provides promising alternatives to traditional strategies in low and high-dimensional settings. We show how the functionality of pec can be extended to yet unsupported prediction models. As an example, we implement support for random forest prediction models based on the R-packages randomSurvivalForest and party. Using data of the Copenhagen Stroke Study we use pec to compare random forests to a Cox regression model derived from stepwise variable selection. Reproducible results on the user level are given for publicly available data from the German breast cancer study group.
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                Author and article information

                Contributors
                jklg@novonordisk.com
                Journal
                Adv Ther
                Adv Ther
                Advances in Therapy
                Springer Healthcare (Cheshire )
                0741-238X
                1865-8652
                27 November 2020
                27 November 2020
                2021
                : 38
                : 1
                : 721-738
                Affiliations
                [1 ]GRID grid.268441.d, ISNI 0000 0001 1033 6139, Unit of Public Health and Preventive Medicine, , Yokohama City University School of Medicine, ; Kanagawa, Japan
                [2 ]GRID grid.26999.3d, ISNI 0000 0001 2151 536X, Department of Health Economics and Outcomes Research, Graduate School of Pharmaceutical Sciences, , The University of Tokyo, ; Tokyo, Japan
                [3 ]GRID grid.425956.9, ISNI 0000 0001 2264 864X, Novo Nordisk A/S, ; Søborg, Denmark
                [4 ]Novo Nordisk Pharma Ltd., Tokyo, Japan
                [5 ]Adelphi Values PROVE, Cheshire, UK
                Article
                1561
                10.1007/s12325-020-01561-1
                7854394
                33245530
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.

                Funding
                Funded by: Novo Nordisk
                Categories
                Original Research
                Custom metadata
                © Springer Healthcare Ltd., part of Springer Nature 2021

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