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Programació de seminaris 2019


  • Divendres, 15 de març de 2019. Hora: 12:30

David Moriña Soler, Barcelona Graduate School of Mathematics (BGSMath) – Departament de Matemàtiques, Universitat Autònoma de Barcelona (UAB)

Intervention analysis for low count time series with applications in public health

  • Divendres, 22 de febrer de 2019. Hora: 12:00

C.F. Jeff Wu, Georgia Institute of Technology, Atlanta, Georgia, USA.

Quality improvement: from autos and chips to nano and bio 

  • Dimarts, 22 de gener de 2019. Hora: 12:30

Sharon-Lise Normand, Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Statistical Approaches to Health Care Quality Assessments


Intervention analysis for low count time series with applications in public health

CONVIDAT: David Moriña Soler
Seminari EIO, ETSEIB (Edifici Eng. Industrial), Planta 6, Campus Sud, Universitat Politècnica de Catalunya, Avda. Diagonal, 647, 08028, Barcelona

DATA:  Divendres, 15 de Març de 2019. Hora: 12:30

RESUM: It is common in many fields to be interested in the evaluation of the impact of an intervention over a particular phenomenon. In the context of classical time series analysis a possible choice might be intervention analysis, but there is no analogous methodology developed for low count time series. In this talk, we will introduce a modified INAR model that allows to quantify the effect of an intervention and is also capable of taking into account possible trends or seasonal behaviour. Several examples of application in different real and simulated contexts will also be discussed.

SOBRE L'AUTOR: David Moriña holds a PhD in Mathematics obtained at Autònoma University in 2013. His area of interest is focused in mathematical modelling applied to health sciences, especially in the handling and analysis of longitudinal data, specifically time series data. He has broad experience in the design, development and analysis of clinical trials and epidemiological studies-working in several research centres, including the Technological Center in Nutrition and Health (CTNS), the Centre for Research in Environmental Epidemiology (CREAL) and the Catalan Institute of Oncology, developing new models for cancer research. He joined BGSMath - UAB in 2018, working on the development of new mathematical and statistical models with applications to cancer epidemiology. For more information on his research, see https://bgsmath.cat/people/?person=david-morina-soler


Quality improvement: from autos and chips to nano and bio 

CONVIDAT: C.F. Jeff Wu IDIOMA: Anglès LLOC: Seminari EIO, ETSEIB (Edifici Eng. Industrial), Planta 6, Campus Sud, Universitat Politècnica de Catalunya, Avda. Diagonal, 647, 08028, Barcelona

DATA: Divendres, 22 de febrer de 2019. Hora: 12:00 

RESUM: Quality improvement (QI) has a glorious history, starting from Shewhart’s path-breaking work on statistical process control to Deming’s high-impact work on quality management. Statistical concepts and tools played a key role in such work. As the applications became more sophisticated, elaborate statistical methods were required to tackle the problems. In the last three decades, QI has seen more use of experimental design and analysis, particularly the methodology of robust parameter design (RPD). I will first review some major ideas in RPD, focusing on its engineering origin and statistical methodology. I will then discuss more recent work that expands the original approach, including the use of feedback control and operating window. To have an effective solution, the subject matter knowledge often needs to be incorporated. Techniques for fusing data with knowledge will be presented. For advanced manufacturing and high-tech applications, there are new challenges and possible paradigm shift posed by three features: large varieties, small volume and high added value. I will speculate on some new directions and technical development. Throughout the talk, the ideas will be illustrated with real examples, ranging from the traditional (autos and chips) to the modern (nano and bio).

SOBRE L'AUTOR: C.F. Jeff Wu is Professor and Coca Cola Chair in Engineering Statistics at the School of Industrial and Systems Engineering, Georgia Institute of Technology. He was the first academic statistician elected to the National Academy of Engineering (2004); also a Member (Academician) of Academia Sinica (2000). A Fellow of American Society for Quality, Institute of Mathematical Statistics, of INFORMS, and American Statistical Association. He received the COPSS (Committee of Presidents of Statistical Societies) Presidents’ Award in 1987, the COPSS Fisher Lecture Award in 2011, the Deming Lecture Award in 2012, the inaugural Akaike Memorial Lecture Award in 2016, the George Box Medal from Enbis in 2017, and numerous other awards and honors. He has published more than 175 research articles and supervised 48 Ph.D.'s. He has published two books "Experiments: Planning, Analysis, and Parameter Design Optimization" (with Hamada) and “A Modern Theory of Factorial Designs” (with Mukerjee).


Statistical Approaches to Health Care Quality Assessments
CONVIDAT:  Sharon-Lise Normand
IDIOMA: Anglès
LLOC: Edifici B6, Sala d'Actes Manuel Martí, Campus Nord, UPC (veure mapa)

DATA:  Dimarts, 22 de Gener de 2019. Hora: 12:30

RESUM:  Health plan, hospital, and physician quality assessments are ubiquitous in the U.S. Hospital assessments in particular are used for licensure, maintenance, and some assessments are the basis for modification of hospital payments.  For instance, in 2017 the U.S. federal government withheld $528 million from 2597 hospitals as part of the Hospital Readmissions Reduction program described in the Affordable Care Act. This talk will describe the key statistical challenges in determining whether a hospital has higher than "expected outcome" including defining the "treatment", determining the counterfactual outcome, characterizing the role of unmeasured confounders, and accounting for data sparsity and uncertainty.

SOBRE L'AUTOR: Sharon-Lise Normand, Ph.D., is S. James Adelstein Professor of Health Care Policy (Biostatistics) in the Department of Health Care Policy at Harvard Medical School and Professor in the Department of Biostatistics at the Harvard TH Chan School of Public Health. Dr. Normand’s research focuses on the development of statistical methods for health services and outcomes research, primarily using Bayesian approaches, including the evaluation of medical devices in randomized and non-randomized settings for  pre- and post-market assessments,  causal inference, provider profiling, evidence synthesis, item response theory, and latent variables analyses. Her application areas include cardiovascular disease, severe mental illness, medical device safety and effectiveness, and medical technology diffusion. She earned her Ph.D. in Biostatistics from the University of Toronto, holds a Master of Science as well as a Bachelor of Science degrees in Statistics from the University of Western Ontario, and completed a post-doctoral fellowship in Health Care Policy at Harvard Medical School.