O Dimitris Rizopolous Professor of Biostatistics Erasmus Medical Center Rotterdam

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O Dimitris Rizopolous Professor of Biostatistics Erasmus Medical Center Rotterdam

List of mHealth Experts Provided by: Walter Dempsey, Ph.D. University of Michigan Methods: Mind the Gap Tuesday, November 5, 2019 • Joint modeling: o Dimitris Rizopolous Professor of Biostatistics Erasmus Medical Center Rotterdam o Jeremy Taylor Professor, Department of Biostatistics School of Public Health University of Michigan o Cecile Proust-Lima Researcher in Biostatistics University of Bordeaux • Flexible longitudinal models in applied to healthcare: o Suchi Saria John C. Malone Assistant Professor Johns Hopkins University o Michal Abrahamowicz Division of Clinical Epidemiology McGill University Health Centre • Sequential Decision Making: o Inbal Nahum Shani Research Associate Professor Co-Director; Data-Science for Dynamic Decision-Making Lab (d3lab) Institute for Social Research University of Michigan o Susan Murphy Professor of Statistics and of Computer Science Harvard University • Missing data and EMA: o Saul Shiffman Professor or Psychology University of Pittsburgh o Stephen Rathbun Epidemiology & Biostatistics Professor College of Public Health University of Georgia o Donald Hedeker Professor of Biostatistics The University of Chicago • General related science: o Bonnie Spring Director, Institute for Public Health and Medicine (IPHAM) Chief of Behavioral Medicine in the Department of Preventive Medicine Professor of Preventive Medicine (Behavioral Medicine), Psychiatry and Behavioral Sciences and Weinberg College of Arts and Sciences Northwestern University o Robin Mermelstein Distinguished Professor of Psychology and IHRP Director Institute for Health Research and Policy o Matthew Nock Edgar Pierce Professor of Psychology, Harvard College Professor, Chair, Department of Psychology, Harvard University Research Scientist, Massachusetts General Hospital Research Scientist, Boston Children's Hospital Harvard University .

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STEVE AMIREAULT, Ph.D
STEVE AMIREAULT, Ph.D. 800 West Stadium Avenue, West Lafayette, IN, 47907, USA Phone: +1 (765)-496-0568; Email: [email protected] Website: Physical Activity Psychology Lab AREA OF EXPERTISE Epidemiology, exercise psychology, health and well-being, maintenance of health behaviors, motivation, physical activity, public health, questionnaire development and refinement, systematic review and meta-analysis, validation studies. CURRENT EMPLOYMENT 2015 - Present Assistant Professor Department of Health and Kinesiology College of Health and Human Sciences Purdue University Lambert Fieldhouse, Room 311A 800 West Stadium Avenue West Lafayette, IN 47907-2046 EDUCATION Note: Title in bracket ‘[…]’ = English translation. 2013-2015 Postdoctoral Fellowship Faculty of Kinesiology & Physical Education, University of Toronto, Toronto, ON, Canada. Supervisor: Dr. Catherine M. Sabiston (Canada Research Chair in Physical Activity and Mental Health) 2013-2015 Faculty of Arts and Science, Department of Psychology, Concordia University, Montréal, QC, Canada. Co-supervisor: Dr. Carsten Wrosch (Canada Research Chair in Aging and Health) 2009-2013 Doctor of Philosophy (Ph.D.) in Kinesiology Faculty of Medicine, Department of Kinesiology, Université Laval, Quebec City, QC, Canada. Supervisor: Dr. Gaston Godin (Canada Research Chair in Behaviour and Health). Dissertation Title : Effet d’une intervention visant à favoriser le maintien de la pratique de l’activité physique [Effectiveness of an intervention to promote the maintenance of physical activity]. 2005-2007 Master in Sciences (M.Sc.) in Kinesiology Faculty of Medicine, Division of Kinesiology, Université Laval, Quebec City, QC, Canada. Supervisor: Dr. Gaston Godin (Canada Research Chair on Behaviour and Health). Co-supervisor: Dr. Louis Pérusse. 2002-2005 Bachelor of Science (B.Sc.) Kinesiology Faculty of Medicine, Division of Kinesiology, Université Laval, Quebec City, QC, Canada.
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Volume 50 • Issue 6 IMS Bulletin September 2021 IMS Presidential Address(es) Each year, at the end of their term, the IMS CONTENTS President gives an address at the Annual Meeting. 1 IMS Presidential Addresses: Because of the pandemic, the Annual Meeting, Regina Liu and Susan Murphy which would have been at the World Congress in Seoul, didn’t happen, and Susan Murphy’s 2–3 Members’ news : Bin Yu, Marloes Maathuis, Jeff Wu, Presidential Address was postponed to this year. Ivan Corwin, Regina Liu, Anru The 2020–21 President, Regina Liu, gave her Zhang, Michael I. Jordan Address by video at the virtual JSM, which incorpo- 6 Profile: Michael Klass rated Susan’s video Address. The transcript of the two addresses is below. 8 Recent papers: Annals of 2020–21 IMS President Regina Liu Probability, Annals of Applied Probability, ALEA, Probability Regina Liu: It’s a great honor to be the IMS President, and to present this IMS and Mathematical Statistics Presidential address in the 2021 JSM. Like many other things in this pandemic time, this year’s IMS Presidential Address will be also somewhat different from those in the 10 IMS Awards past. Besides being virtual, it will be also given jointly by the IMS Past President Susan 11 IMS London meeting; Olga Murphy and myself. The pandemic prevented Susan from giving her IMS Presidential Taussky-Todd Lecture; Address last year. In keeping with the IMS tradition, I invited Susan to join me today, Observational Studies and I am very pleased she accepted my invitation. Now, Susan will go first with her 12 Sound the Gong: Going 2020 IMS Presidential Address.
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STATISTICS in the DATA SCIENCE ERA: a Symposium to Celebrate 50 Years of Statistics at the University of Michigan
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Optimal Dynamic Treatment Regimes and Partial Welfare Ordering∗ Sukjin (Vincent) Han Department of Economics University of Bristol First Draft: August 8, 2019 This Draft: May 31, 2021 Abstract Dynamic treatment regimes are treatment allocations tailored to heterogeneous in- dividuals. The optimal dynamic treatment regime is a regime that maximizes counter- factual welfare. We introduce a framework in which we can partially learn the optimal dynamic regime from observational data, relaxing the sequential randomization as- sumption commonly employed in the literature but instead using (binary) instrumental variables. We propose the notion of sharp partial ordering of counterfactual welfares with respect to dynamic regimes and establish mapping from data to partial ordering via a set of linear programs. We then characterize the identified set of the optimal regime as the set of maximal elements associated with the partial ordering. We relate the notion of partial ordering with a more conventional notion of partial identifica- tion using topological sorts. Practically, topological sorts can be served as a policy menu for a policymaker. We apply our method to understand returns to schooling and post-school training as a sequence of treatments by combining data from multiple sources. The framework of this paper can be used beyond the current context, e.g., in establishing rankings of multiple treatments or policies across different counterfactual scenarios. ∗For helpful comments and discussions, the author is grateful to Donald Andrews, Isaiah
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Copyright by Susan Laura Murphy 2019 The Dissertation Committee for Susan Laura Murphy certifies that this is the approved version of the following dissertation: An Evaluation of the Feedback Report for the Preventive Resources Inventory APPROVED BY SUPERVISING COMMITTEE: Christopher J. McCarthy, supervisor Karen D. French Timothy Z. Keith Richard Lambert Delida Sanchez An Evaluation of the Feedback Report for the Preventive Resources Inventory by Susan Laura Murphy Dissertation Presented to the Faculty of the Graduate School of The University of Texas at Austin in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy The University of Texas at Austin August 2019 Abstract An Evaluation of the Feedback Report for the Preventive Resources Inventory Susan Laura Murphy, Ph.D. The Univeristy of Texas at Austin, 2019 Supervisor: Christopher J. McCarthy Research on how individuals cope with stress has spanned numerous academic and scientific disciplines, including the fields of counseling and psychology. Investigations have more recently focused on preventive coping, or the coping strategies used by individuals to manage existing stressors and prepare for future demands. The Preventive Resource Inventory (PRI) was developed to assess coping resources for mitigating or preventing stress, rather than withstanding it. The PRI was recently revised to reflect more current theoretical perspectives in stress and coping research, including the influence of positive psychology. This revision process involved developing and testing new items and later conducting a factor analysis to create an updated measure. The present study used the updated version of the PRI to assess the utility of a feedback report for PRI users.
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Dynamic Treatment Regimens
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Discussion on the Paper by Murphy 355 Directed Acyclic Graphs
Discussion on the Paper by Murphy 355 directed acyclic graphs. In Proc. 13th Conf. Uncertainty in Artificial Intelligence (eds D. Geiger and P. Shenoy), pp. 409–442. San Francisco: Morgan Kaufmann. Rubin, D. B. (1978) Bayesian inference for causal effects: the role of randomization. Ann. Statist., 6, 34–58. (1986) Which ifs have causal answers. J. Am. Statist. Ass., 81, 961–962. Shachter, R. D. (1986) Evaluating influence diagrams. Ops Res., 34, 871–882. Discussion on the paper by Murphy Elja Arjas .University of Helsinki/ If I am right the burning issue in social and educational programmes, of which the Fast Track study is an example, is not how to determine an individually optimal dynamic treatment regime but, rather, how a limited total resource should be optimally shared between those who need help. Treatments cannot then be assigned purely on the basis of individual status and treatment histories. The situation is different in clini- cal trials, where the availability of drugs is not a problem but where they often have unwanted side-effects. The simple logic ‘more is better’ does not then necessarily apply. My second comment deals with the concept of potential outcomes and the no unmeasured confounders postulate. I understand that it is tempting to postulate the existence of individual potential outcomes that are indexed by a list of treatments received, the main advantage being that the appealing concept of an ‘individual causal effect’ associated with one regime versus another can then be defined directly as the contrast between the corresponding potential outcomes. But I have great difficulty in understanding what the postulate of no unmeasured confounders, as formulated in the paper, would mean in a concrete study.
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A Sample Size Calculator for SMART Pilot Studies
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