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1دورية أكاديمية
المؤلفون: Srdan Verstovsek, Ivan Krečak, Florian H. Heidel, Valerio De Stefano, Kenneth Bryan, Mike W. Zuurman, Michael Zaiac, Mara Morelli, Aoife Smyth, Santiago Redondo, Erwan Bigan, Michael Ruhl, Christoph Meier, Magali Beffy, Jean-Jacques Kiladjian
المصدر: Biomedicines, Vol 11, Iss 7, p 1925 (2023)
مصطلحات موضوعية: polycythemia vera, thromboembolic events, machine learning, real-world evidence, hydroxyurea, ruxolitinib, Biology (General), QH301-705.5
الوصف: Patients with polycythemia vera (PV) are at significant risk of thromboembolic events (TE). The PV-AIM study used the Optum® de-identified Electronic Health Record dataset and machine learning to identify markers of TE in a real-world population. Data for 82,960 patients with PV were extracted: 3852 patients were treated with hydroxyurea (HU) only, while 130 patients were treated with HU and then changed to ruxolitinib (HU-ruxolitinib). For HU-alone patients, the annualized incidence rates (IR; per 100 patients) decreased from 8.7 (before HU) to 5.6 (during HU) but increased markedly to 10.5 (continuing HU). Whereas for HU-ruxolitinib patients, the IR decreased from 10.8 (before HU) to 8.4 (during HU) and was maintained at 8.3 (after switching to ruxolitinib). To better understand markers associated with TE risk, we built a machine-learning model for HU-alone patients and validated it using an independent dataset. The model identified lymphocyte percentage (LYP), neutrophil percentage (NEP), and red cell distribution width (RDW) as key markers of TE risk, and optimal thresholds for these markers were established, from which a decision tree was derived. Using these widely used laboratory markers, the decision tree could be used to identify patients at high risk for TE, facilitate treatment decisions, and optimize patient management.
وصف الملف: electronic resource
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2دورية أكاديمية
المؤلفون: Manuella Baraton, Magali Beffy, Denis Fougère
المساهمون: Institut national de la statistique et des études économiques
المصدر: Economie et statistique 441-442(1):55-78
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3دورية أكاديمية
المؤلفون: Marion Bachelet, Magali Beffy, Didier Blanchet
المساهمون: Institut national de la statistique et des études économiques
المصدر: Economie et statistique 441-442(1):123-143
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4دورية أكاديمية
المؤلفون: Magali Beffy, Denis Fougère, Arnaud Maurel
المساهمون: Institut national de la statistique et des études économiques
المصدر: Economie et statistique 422(1):31-50
الوصف: Cet article est consacré à l’estimation des effets du travail salarié des étudiants sur leur réussite universitaire et leur décision de poursuite d’études. L’analyse repose sur des échantillons extraits des enquêtes Emploi conduites par l’Insee de 1992 à 2002. Ces échantillons sont restreints aux personnes en cours d’études initiales à l’université et préparant un diplôme universitaire de premier ou de second cycle (Deug, licence ou maîtrise). Sont exclus de l’analyse les étudiants dont l’emploi va de pair avec les études, en particulier les apprentis sous contrat et les stagiaires en formation. Les modèles estimés sont des modèles de type Probit à deux équations simultanées, la première expliquant l’occupation d’un emploi salarié par l’étudiant, la seconde sa réussite à l’examen de fin d’année, conjointement avec sa décision de poursuite des études pour l’un des modèles. Le temps de travail salarié est pris en compte en distinguant, dans un des modèles, les emplois de moins ou plus de 16 heures par semaine. Les résultats montrent que l’occupation d’un emploi régulier réduit significativement la probabilité de réussite à l’examen de fin d’année universitaire. S’ils ne travaillaient pas, les étudiants salariés auraient une probabilité plus élevée de 43 points de réussir leur année. Une analyse complémentaire montre que le cumul emploi-études n’a pas d’effet significatif sur la probabilité de poursuivre les études l’année suivante, quels que soient la filière et le niveau des études.
This article reports an estimation of the effects of students’ paid employment on their success at university and their decision to pursue their studies. Our analysis is based on samples extracted from INSEE Labour Force Surveys conducted between 1992 and 2002. The samples are confined to students who have begun their university studies and are preparing a first-or second-stage degree (French DEUG or B. A. or M. A. equivalent). We exclude students whose jobs are linked to their studies, particularly apprentices under contract and interns in training. We estimate probit models with two simultaneous equations: the first explains the student’s paid employment; the second explains the student’s success in the year-end exam and —in one of the models— jointly with the decision to continue his or her studies. The models incorporate working time in paid employment: one of the models distinguishes between jobs requiring more or less than 16 hours a week. The results show that holding a steady job significantly reduces the probability of passing exams at the end of the academic year. If they did not work, students in paid employment would have a 43-point-higher probability of completing their academic year successfully. An additional analysis shows that the job-plus-studies combination does not significantly influence the probability of pursuing one’s studies in the following year, regardless of program and academic level.
In diesem Artikel werden die Auswirkungen einer lohnabhängigen Erwerbstätigkeit der Studenten auf den Erfolg ihres Hochschulstudiums und die Entscheidung, es fortzusetzen, analysiert. Die Studie basiert auf Stichproben aus den Insee-Beschäftigungserhebungen zwischen 1992 und 2002. Diese Stichproben beschränken sich auf die Personen, die mit ihrem Studium gerade begonnen haben und ein Diplom des Grund-oder Hauptstudiums vorbereiten (Bachelor oder Master). Nicht berücksichtigt in der Analyse sind Studenten, deren Erwerbstätigkeit Teil ihrer Ausbildung ist, insbesondere Lehrlinge mit einem Ausbildungsvertrag und in Ausbildung befindliche Praktikanten. Geschätzt werden Modelle des Typs Probit mit zwei gleichzeitigen Gleichungen: Die erste erklärt die lohnabhängige Erwerbstätigkeit eines Studenten und die zweite seinen Erfolg bei der Prüfung zu Jahresende, und zwar für eines der Modelle gemeinsam mit seiner Entscheidung, sein Studium fortzusetzen. Die Arbeitszeit als Lohnabhängiger wird berücksichtigt, wobei bei einem der Modelle zwischen einer Beschäftigung von mehr oder weniger als 16 Wochenstunden unterschieden wird. Die Ergebnisse zeigen, dass eine regelmäßige Erwerbstätigkeit die Wahrscheinlichkeit einer erfolgreichen Absolvierung der Prüfung zu Ende des Hochschuljahrs erheblich mindert. Würden die erwerbstätigen Studenten nicht arbeiten, wäre die Wahrscheinlichkeit eines erfolgreichen Abschlusses des Hochschuljahrs um 43 Prozentpunkte größer. Eine ergänzende Studie gelangt zu dem Schluss, dass die Kumulierung von Erwerbstätigkeit und Studium keinen nennenswerten Einfluss auf die Wahrscheinlichkeit hat, dass das Studium im darauffolgenden Jahr fortgesetzt wird, und zwar unabhängig vom Studiengang und vom Studienniveau.
El presente artículo se centra en valorar los efectos del trabajo asalariado de los estudiantes en relación con el éxito en los estudios universitarios y su decisión de continuar dichos estudios. El análisis se basa en muestras extraídas de las encuestas sobre Empleo llevadas a cabo por el Insee de 1992 a 2002. Estas muestras se limitan a las personas que están realizando estudios iniciales en la universidad y preparando una titulación de primer o segundo ciclo (diplomas correspondientes a dos, tres o cuatro años de estudios universitarios). Se excluyen del análisis los estudiantes cuyo empleo acompaña los estudios, en particular los aprendices con contrato y los cursillistas en formación. Los modelos estimados son modelos de tipo Probit con dos ecuaciones simultáneas, la primera explicando la ocupación de un empleo asalariado por el estudiante, y la segunda explicando su éxito en el examen final, junto a su decisión de continuar los estudios para uno de los modelos. El tiempo de trabajo asalariado se considera diferenciando, en uno de los modelos, los empleos de menos o más de 16 horas por semana. Los resultados muestran que la ocupación de un empleo regular reduce significativamente la probabilidad de éxito en el examen final universitario. Si no trabajaran, los estudiantes asalariados tendrían una probabilidad más elevada de 43 puntos de aprobar el año. Un análisis complementario muestra que la suma empleo-estudios no tiene efecto significativo en la probabilidad de continuar los estudios el año siguiente, sea cual sea el sector y el nivel de estudios. -
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المؤلفون: Kiladjian, Srdan Verstovsek, Ivan Krečak, Florian H. Heidel, Valerio De Stefano, Kenneth Bryan, Mike W. Zuurman, Michael Zaiac, Mara Morelli, Aoife Smyth, Santiago Redondo, Erwan Bigan, Michael Ruhl, Christoph Meier, Magali Beffy, Jean-Jacques
المصدر: Biomedicines; Volume 11; Issue 7; Pages: 1925
مصطلحات موضوعية: polycythemia vera, thromboembolic events, machine learning, real-world evidence, hydroxyurea, ruxolitinib, biomarkers
الوصف: Patients with polycythemia vera (PV) are at significant risk of thromboembolic events (TE). The PV-AIM study used the Optum® de-identified Electronic Health Record dataset and machine learning to identify markers of TE in a real-world population. Data for 82,960 patients with PV were extracted: 3852 patients were treated with hydroxyurea (HU) only, while 130 patients were treated with HU and then changed to ruxolitinib (HU-ruxolitinib). For HU-alone patients, the annualized incidence rates (IR; per 100 patients) decreased from 8.7 (before HU) to 5.6 (during HU) but increased markedly to 10.5 (continuing HU). Whereas for HU-ruxolitinib patients, the IR decreased from 10.8 (before HU) to 8.4 (during HU) and was maintained at 8.3 (after switching to ruxolitinib). To better understand markers associated with TE risk, we built a machine-learning model for HU-alone patients and validated it using an independent dataset. The model identified lymphocyte percentage (LYP), neutrophil percentage (NEP), and red cell distribution width (RDW) as key markers of TE risk, and optimal thresholds for these markers were established, from which a decision tree was derived. Using these widely used laboratory markers, the decision tree could be used to identify patients at high risk for TE, facilitate treatment decisions, and optimize patient management.
وصف الملف: application/pdf
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=multidiscipl::90c092aa7e0e916ca4233d296893ed64Test
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المؤلفون: Magali Beffy, Lénaïc Couderc, Alizé Papp
المصدر: Regards croisés sur l'économie. :149-155
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::32a4a241b4f90b1395dc06f260f579caTest
https://doi.org/10.3917/rce.023.0149Test -
7دورية أكاديمية
المؤلفون: Magali Beffy, Denis Fougère
المساهمون: The Pennsylvania State University CiteSeerX Archives
مصطلحات موضوعية: bivariate probit models, post-sec
الوصف: www.cepr.org Available online at: www.cepr.org/pubs/dps/DP9565.asp www.ssrn.com/xxx/xxx/xxx
وصف الملف: application/pdf
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8دورية أكاديمية
المؤلفون: Magali Beffy, Denis Fougère, Arnaud Maurel
المساهمون: The Pennsylvania State University CiteSeerX Archives
المصدر: http://ftp.iza.org/dp5069.pdfTest.
الوصف: (ii) development of
وصف الملف: application/pdf
العلاقة: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.501.2303Test; http://ftp.iza.org/dp5069.pdfTest
الإتاحة: http://ftp.iza.org/dp5069.pdfTest
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المؤلفون: Magali Beffy, Srdan Verstovsek, Mike Zuurman, Kenneth Bryan, A. Mathur, Christoph Meier, Michael Ruhl, Jean-Jacques Kiladjian, Michael Zaiac, Florian H. Heidel, Valerio De Stefano, Mara Morelli, Erwan Bigan, Brian Buckley
المصدر: Blood. 136:45-46
مصطلحات موضوعية: business.industry, Incidence (epidemiology), Immunology, Context (language use), Red blood cell distribution width, Cell Biology, Hematology, medicine.disease, Machine learning, computer.software_genre, Biochemistry, Polycythemia vera, Cohort, Propensity score matching, medicine, Current employment, Artificial intelligence, Diagnosis code, business, computer
الوصف: Aims: We previously reported results of the first machine learning study to identify the novel biomarkers associated with crude incidence of TEs in PV patients (pts) treated with hydroxyurea (HU) (Verstovsek et al, Blood 2019). In the current study, we have expanded the database to perform an in-depth machine learning analysis of biomarkers predicting the annual standardized incidence rate (IR) of TEs in PV pts treated with HU. In addition, we examined the IR of TEs in HU-treated pts and pts treated first with HU, then switched to ruxolitinib (HU-RUX). Methods: The study is part of PV-AIM (polycythemia vera advanced integrated models for prevention of TEs). The US OPTUM database includes the electronic medical records from 105 million pts (2007-2020), including 82 960 PV pts with a median record length of 8.4 years. TEs were assessed (A) before the treatment (Tx) initiation in both groups; (B) while on HU (median: 29 months) and (C) while continuing HU or switching to RUX (median, 17 months). A total of 3852 HU-alone pts and 130 HU-RUX pts passed inclusion/exclusion criteria. To avoid selection bias, only HU-alone pts treated prior to RUX market launch were included (n=704). Cohorts were then matched by selecting the nearest cases from the HU-alone groups based on the propensity scores calculated from the total Tx period, gender, race, age at index, and geographical division (n=130 pts in both HU-alone and HU-RUX). TEs were identified from a restrictive list of ICD-CM diagnosis codes defined in the RESPONSE study (Kiladjian et al, Lancet Haematol 2020). Annualized IR of TEs were then calculated per 100 pts for each of the above Tx periods for each cohort. A random survival forest (RSF) model was then constructed for HU-alone pts, with at least 6 months of HU Tx and 18 months of follow-up, to predict the risk of TEs 6 to 18 months after the first HU Tx. Pts with at least 1 lab test and 1 observation obtained at 3 to 6 months post index were included in the model (n=1012). The features also included demographics, history of TEs, phlebotomy and anti-coagulant use. The performance was assessed on a 70:30 train:test split using Receiver Operating Characteristic-Area Under the Curve (ROC-AUC). RSF variable importance was then used to identify the variables with the largest impact on prediction of TEs. The feature interactions for the top 10 features were mapped and assessed in terms of risk of TEs via log-rank using a brute-force combinatorial approach. Risk was assessed in the context of total population and in pts with/without a history of TEs. Optimal interactions were presented as interpretable 4-group Kaplan-Meier plots. Lastly, the feature interactions were scored for synergy in the context of risk of TEs based on the difference in expected and observed pairwise probabilities. Results: The RSF model achieved an accuracy of > 0.80 AUC for prediction of TEs 6 to12 months after the first HU Tx. RSF variable importance revealed that the history of TEs had the largest effect on prediction (>2-fold) relative to other features. The top observational and lab variables in terms of feature importance were body mass index and neutrophil percentage (NEP), respectively, followed by the previously observed lymphocyte percentage (LYP) and RDW. The largest synergistic pairwise interactions (Table 1) were NEP + RDW (Figure 1a), followed by LYP + RDW (Figure 1b), which occurred in pts without history of TEs. The pre-Tx baseline incidence of TEs in the HU-alone and HU-RUX groups were 8.7 and 10.8, respectively. The rates of TEs decreased by 36% and 22% to 5.6 and 8.4, respectively during the HU Tx. Following the switch to RUX, the rate of TEs was maintained in the HU-RUX group at 8.3 (−1%) but increased to 10.53 (+89%) in the HU-alone group during the same period of 17 months (Figure 2). Conclusion: An in-depth machine learning analysis to identify the markers that predict TEs in HU-treated PV pts revealed interaction of key hematological parameters with RDW. This finding could help recognize the pts at high risk of TE and possibly prevent a major cause of morbidity and mortality. The results reconfirm the role of RDW, as well as NEP/LYM ratios in predicting TE occurrence, routine lab parameters in the clinic, but not usually taken into account. Finally, we observed a renewed increase in TEs in HU-treated pts after an initial decrease, which was not the case in pts switching to RUX in the same period. We aim to present similar biomarker analyses on RUX pts in the final presentation. Disclosures Verstovsek: Promedior: Research Funding; NS Pharma: Research Funding; PharmaEssentia: Research Funding; Novartis: Consultancy, Research Funding; Sierra Oncology: Consultancy, Research Funding; AstraZeneca: Research Funding; Blueprint Medicines Corp: Research Funding; Genentech: Research Funding; CTI Biopharma Corp: Research Funding; ItalPharma: Research Funding; Incyte Corporation: Consultancy, Research Funding; Protagonist Therapeutics: Research Funding; Roche: Research Funding; Gilead: Research Funding; Celgene: Consultancy, Research Funding. De Stefano:Janssen Cilag: Other: Non-financial support; Celgene: Other: Non-financial support, personal fee; Bayer: Other: Non-financial support; Amgen: Other: Personal fee; Novartis: Other: Personal fee, Research Funding. Heidel:Novartis: Consultancy, Honoraria, Research Funding. Zuurman:Novartis Pharma A.G.: Current Employment. Zaiac:Novartis: Current Employment, Current equity holder in private company. Bryan:Novartis: Current Employment. Buckley:Novartis: Current Employment. Mathur:Novartis: Current Employment. Morelli:Novartis: Current Employment. Bigan:Novartis: Consultancy. Ruhl:Boston Consulting Group: Current Employment. Meier:Novartis: Consultancy.
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::3ba2356cccb69fef2e2a4b2cb9811bc3Test
https://doi.org/10.1182/blood-2020-137358Test -
10دورية أكاديمية
المؤلفون: Magali Beffy, Denis Fougère, Arnaud Maurel
المساهمون: The Pennsylvania State University CiteSeerX Archives
الوصف: IZA is an independent nonprofit
وصف الملف: application/pdf
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