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      Patient Preference and Adherence (submit here)

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      Patients’ and physicians’ preferences for type 2 diabetes mellitus treatments in Spain and Portugal: a discrete choice experiment

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          Abstract

          Objective

          To assess Spanish and Portuguese patients’ and physicians’ preferences regarding type 2 diabetes mellitus (T2DM) treatments and the monthly willingness to pay (WTP) to gain benefits or avoid side effects.

          Methods

          An observational, multicenter, exploratory study focused on routine clinical practice in Spain and Portugal. Physicians were recruited from multiple hospitals and outpatient clinics, while patients were recruited from eleven centers operating in the public health care system in different autonomous communities in Spain and Portugal. Preferences were measured via a discrete choice experiment by rating multiple T2DM medication attributes. Data were analyzed using the conditional logit model.

          Results

          Three-hundred and thirty (n=330) patients (49.7% female; mean age 62.4 [SD: 10.3] years, mean T2DM duration 13.9 [8.2] years, mean body mass index 32.5 [6.8] kg/m 2, 41.8% received oral + injected medication, 40.3% received oral, and 17.6% injected treatments) and 221 physicians from Spain and Portugal (62% female; mean age 41.9 [SD: 10.5] years, 33.5% endocrinologists, 66.5% primary-care doctors) participated. Patients valued avoiding a gain in bodyweight of 3 kg/6 months (WTP: €68.14 [95% confidence interval: 54.55–85.08]) the most, followed by avoiding one hypoglycemic event/month (WTP: €54.80 [23.29–82.26]). Physicians valued avoiding one hypoglycemia/week (WTP: €287.18 [95% confidence interval: 160.31–1,387.21]) the most, followed by avoiding a 3 kg/6 months gain in bodyweight and decreasing cardiovascular risk (WTP: €166.87 [88.63–843.09] and €154.30 [98.13–434.19], respectively). Physicians and patients were willing to pay €125.92 (73.30–622.75) and €24.28 (18.41–30.31), respectively, to avoid a 1% increase in glycated hemoglobin, and €143.30 (73.39–543.62) and €42.74 (23.89–61.77) to avoid nausea.

          Conclusion

          Both patients and physicians in Spain and Portugal are willing to pay for the health benefits associated with improved diabetes treatment, the most important being to avoid hypoglycemia and gaining weight. Decreased cardiovascular risk and weight reduction became the third most valued attributes for physicians and patients, respectively.

          Most cited references48

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          Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future.

          Glucose metabolism is normally regulated by a feedback loop including islet β cells and insulin-sensitive tissues, in which tissue sensitivity to insulin affects magnitude of β-cell response. If insulin resistance is present, β cells maintain normal glucose tolerance by increasing insulin output. Only when β cells cannot release sufficient insulin in the presence of insulin resistance do glucose concentrations rise. Although β-cell dysfunction has a clear genetic component, environmental changes play an essential part. Modern research approaches have helped to establish the important role that hexoses, aminoacids, and fatty acids have in insulin resistance and β-cell dysfunction, and the potential role of changes in the microbiome. Several new approaches for treatment have been developed, but more effective therapies to slow progressive loss of β-cell function are needed. Recent findings from clinical trials provide important information about methods to prevent and treat type 2 diabetes and some of the adverse effects of these interventions. However, additional long-term studies of drugs and bariatric surgery are needed to identify new ways to prevent and treat type 2 diabetes and thereby reduce the harmful effects of this disease. Copyright © 2014 Elsevier Ltd. All rights reserved.
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            Sample Size Requirements for Discrete-Choice Experiments in Healthcare: a Practical Guide

            Discrete-choice experiments (DCEs) have become a commonly used instrument in health economics and patient-preference analysis, addressing a wide range of policy questions. An important question when setting up a DCE is the size of the sample needed to answer the research question of interest. Although theory exists as to the calculation of sample size requirements for stated choice data, it does not address the issue of minimum sample size requirements in terms of the statistical power of hypothesis tests on the estimated coefficients. The purpose of this paper is threefold: (1) to provide insight into whether and how researchers have dealt with sample size calculations for healthcare-related DCE studies; (2) to introduce and explain the required sample size for parameter estimates in DCEs; and (3) to provide a step-by-step guide for the calculation of the minimum sample size requirements for DCEs in health care. Electronic supplementary material The online version of this article (doi:10.1007/s40271-015-0118-z) contains supplementary material, which is available to authorized users.
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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

                Journal
                Patient Prefer Adherence
                Patient Prefer Adherence
                Patient Preference and Adherence
                Patient preference and adherence
                Dove Medical Press
                1177-889X
                2015
                14 October 2015
                : 9
                : 1443-1458
                Affiliations
                [1 ]Hospital Universitario Dr Peset, Valencia, Spain
                [2 ]USF São Domingos, Santarém, Portugal
                [3 ]Hospital Montecelo de Pontevedra, Galicia, Spain
                [4 ]USF Porta do Sol, Matosinhos, Portugal
                [5 ]Hospital Universitario Principe de Asturias, Madrid, Spain
                [6 ]USF Serra da Lousã, Lousã, Portugal
                [7 ]Hospital Clinic, Barcelona, Spain
                [8 ]Hospital de Navarra, Navarra, Spain
                [9 ]Outcomes’10, Universidad Jaume I, Castellón, Spain
                [10 ]Hospital Universitario Nuestra Señora de la Candelaria, Canarias, Spain
                [11 ]Hospital Universitario Virgen de la Macarena, Sevilla, Spain
                [12 ]INCLIVA, CIBERESP, Universidad de Valencia, Valencia, Spain
                [13 ]Sociedad Española de Medicina Familiar y Comunitaria, Valencia, Spain
                [14 ]Novo Nordisk EU-HEOR Europe, Madrid, Spain
                [15 ]Novo Nordisk, Lisbon, Portugal
                Author notes
                Correspondence: Antonio Ramirez de Arellano, Novo Nordisk Pharma SA, Via de los Poblados, 3, Parque Empresarial Cristalia, Edificio 6 – 4ª Planta, 28033 Madrid, Spain, Tel +34 913 349 800, Email http://www.novonordisk.es
                Article
                ppa-9-1443
                10.2147/PPA.S88022
                4612138
                26508841
                0eb3fcbd-49bf-438c-9c13-9622cc642265
                © 2015 Morillas et al. This work is published by Dove Medical Press Limited, and licensed under Creative Commons Attribution – Non Commercial (unported, v3.0) License

                The full terms of the License are available at http://creativecommons.org/licenses/by-nc/3.0/. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.

                History
                Categories
                Original Research

                Medicine
                diabetes,discrete choice model,preferences,willingness to pay,hypoglycemia,weight,cardiovascular risk,hba1c

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