Interim Monitoring in Sequential Multiple Assignment Randomized
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Sequential Analysis Tests and Confidence Intervals
D. Siegmund Sequential Analysis Tests and Confidence Intervals Series: Springer Series in Statistics The modern theory of Sequential Analysis came into existence simultaneously in the United States and Great Britain in response to demands for more efficient sampling inspection procedures during World War II. The develop ments were admirably summarized by their principal architect, A. Wald, in his book Sequential Analysis (1947). In spite of the extraordinary accomplishments of this period, there remained some dissatisfaction with the sequential probability ratio test and Wald's analysis of it. (i) The open-ended continuation region with the concomitant possibility of taking an arbitrarily large number of observations seems intol erable in practice. (ii) Wald's elegant approximations based on "neglecting the excess" of the log likelihood ratio over the stopping boundaries are not especially accurate and do not allow one to study the effect oftaking observa tions in groups rather than one at a time. (iii) The beautiful optimality property of the sequential probability ratio test applies only to the artificial problem of 1985, XI, 274 p. testing a simple hypothesis against a simple alternative. In response to these issues and to new motivation from the direction of controlled clinical trials numerous modifications of the sequential probability ratio test were proposed and their properties studied-often Printed book by simulation or lengthy numerical computation. (A notable exception is Anderson, 1960; see III.7.) In the past decade it has become possible to give a more complete theoretical Hardcover analysis of many of the proposals and hence to understand them better. ▶ 119,99 € | £109.99 | $149.99 ▶ *128,39 € (D) | 131,99 € (A) | CHF 141.50 eBook Available from your bookstore or ▶ springer.com/shop MyCopy Printed eBook for just ▶ € | $ 24.99 ▶ springer.com/mycopy Order online at springer.com ▶ or for the Americas call (toll free) 1-800-SPRINGER ▶ or email us at: [email protected]. -
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. -
IMS Presidential Address(Es) Each Year, at the End of Their Term, the IMS CONTENTS President Gives an Address at the Annual Meeting
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. -
Trial Sequential Analysis: Novel Approach for Meta-Analysis
Anesth Pain Med 2021;16:138-150 https://doi.org/10.17085/apm.21038 Review pISSN 1975-5171 • eISSN 2383-7977 Trial sequential analysis: novel approach for meta-analysis Hyun Kang Received April 19, 2021 Department of Anesthesiology and Pain Medicine, Chung-Ang University College of Accepted April 25, 2021 Medicine, Seoul, Korea Systematic reviews and meta-analyses rank the highest in the evidence hierarchy. However, they still have the risk of spurious results because they include too few studies and partici- pants. The use of trial sequential analysis (TSA) has increased recently, providing more infor- mation on the precision and uncertainty of meta-analysis results. This makes it a powerful tool for clinicians to assess the conclusiveness of meta-analysis. TSA provides monitoring boundaries or futility boundaries, helping clinicians prevent unnecessary trials. The use and Corresponding author Hyun Kang, M.D., Ph.D. interpretation of TSA should be based on an understanding of the principles and assump- Department of Anesthesiology and tions behind TSA, which may provide more accurate, precise, and unbiased information to Pain Medicine, Chung-Ang University clinicians, patients, and policymakers. In this article, the history, background, principles, and College of Medicine, 84 Heukseok-ro, assumptions behind TSA are described, which would lead to its better understanding, imple- Dongjak-gu, Seoul 06974, Korea mentation, and interpretation. Tel: 82-2-6299-2586 Fax: 82-2-6299-2585 E-mail: [email protected] Keywords: Interim analysis; Meta-analysis; Statistics; Trial sequential analysis. INTRODUCTION termined number of patients was reached [2]. A systematic review is a research method that attempts Sequential analysis is a statistical method in which the fi- to collect all empirical evidence according to predefined nal number of patients analyzed is not predetermined, but inclusion and exclusion criteria to answer specific and fo- sampling or enrollment of patients is decided by a prede- cused questions [3]. -
Detecting Reinforcement Patterns in the Stream of Naturalistic Observations of Social Interactions
Portland State University PDXScholar Dissertations and Theses Dissertations and Theses 7-14-2020 Detecting Reinforcement Patterns in the Stream of Naturalistic Observations of Social Interactions James Lamar DeLaney 3rd Portland State University Follow this and additional works at: https://pdxscholar.library.pdx.edu/open_access_etds Part of the Developmental Psychology Commons Let us know how access to this document benefits ou.y Recommended Citation DeLaney 3rd, James Lamar, "Detecting Reinforcement Patterns in the Stream of Naturalistic Observations of Social Interactions" (2020). Dissertations and Theses. Paper 5553. https://doi.org/10.15760/etd.7427 This Thesis is brought to you for free and open access. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. Detecting Reinforcement Patterns in the Stream of Naturalistic Observations of Social Interactions by James Lamar DeLaney 3rd A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Psychology Thesis Committee: Thomas Kindermann, Chair Jason Newsom Ellen A. Skinner Portland State University i Abstract How do consequences affect future behaviors in real-world social interactions? The term positive reinforcer refers to those consequences that are associated with an increase in probability of an antecedent behavior (Skinner, 1938). To explore whether reinforcement occurs under naturally occuring conditions, many studies use sequential analysis methods to detect contingency patterns (see Quera & Bakeman, 1998). This study argues that these methods do not look at behavior change following putative reinforcers, and thus, are not sufficient for declaring reinforcement effects arising in naturally occuring interactions, according to the Skinner’s (1938) operational definition of reinforcers. -
STATISTICS in the DATA SCIENCE ERA: a Symposium to Celebrate 50 Years of Statistics at the University of Michigan
STATISTICS IN THE DATA SCIENCE ERA: A Symposium to Celebrate 50 Years of Statistics at the University of Michigan September 20-21, 2019 Rackham Graduate School 915 E. Washington Street Ann Arbor, MI 48109 A Symposium to Celebrate 50 Years of Statistics at the University of Michigan 1 PROGRAM Friday, September 20, 2019 Saturday, September 21, 2019 9:30-10:30 ...............Coffee and Registration 8:30-9:00 ................ Coffee 10:30-10:50 ..............Opening Remarks Invited Session 2 Chair: Snigdha Panigrahi, University of Michigan Xuming He and Dean Anne Curzan 9:00-9:30 ................ Adam Rothman, University of Minnesota Shrinking Characteristics of Precision Matrix Keynote Session 1 Chair: Ambuj Tewari, University of Michigan Estimators 10:50-11:50 ...............Susan Murphy, Harvard University Online Experimentation with Learning Algorithms 9:30-10:00 ............... Bodhisattva Sen, Columbia University in a Clinical Trial Multivariate Rank-based Distribution-free Nonparametric Testing using Measure 11:50-12:00 ...............Group Photo Transportation PROGRAM 12:00-1:30 ................Lunch and Poster Session 10:00-10:30 ............. Min Qian, Columbia University Personalized Policy Learning using Longitudinal University of Michigan Invited Session 1 Chair: Ziwei Zhu, Mobile Health Data 1:30-2:00 .................Sumanta Basu, Cornell University Large Spectral Density Matrix Estimation by 10:30-11:00 .............. Coffee Break Thresholding Invited Session 3 Chair: Kean Ming Tan, University of Michigan 2:00-2:30 .................Anindya Bhadra, Purdue University 11:00-11:30 ............... Ali Shojaie, University of Washington Horseshoe Regularization for Machine Learning in Incorporating Auxiliary Information into Learning Complex and Deep Models Directed Acyclic Graphs 2:30-3:00 .................Coffee Break 11:30-12:00 .............. -
Smarts: Part I
SMARTs: Part I Eric B. Laber Department of Statistics, North Carolina State University April 2019 SAMSI Warm up part I: quiz! I Discuss with your stat buddy: I What is a clinical trial? What's the best way to quantify the shame you should feel if you don't know? I What is a power calculation? I What are common complicating statistical issues associated with clinical trials? I True or false I Sequential Hierarchically Assigned Randomization Trials are the gold standard design for estimation and evaluation of treatment regimes. I Susan Murphy, who authored several seminal papers on sequential clinical trial design, is affectionately known as `Smurphy' to her friends and colleagues. I The legend of Santa Clause may be partially based on Siberian Shamans consuming psychedelic mushrooms with reindeer. 1 / 64 Warm up: quiz! I Discuss with your stat buddy: I What is a clinical trial? What's the best way to quantify the shame you should feel if you don't know? I What is a power calculation? I What are common complicating statistical issues associated with clinical trials? I True or false I Sequential Hierarchically Assigned Randomization Trials are the gold standard design for estimation and evaluation of treatment regimes. I Susan Murphy, who authored several seminal papers on sequential clinical trial design, is affectionately known as `Smurphy' to her friends and colleagues. I The legend of Santa Clause may be partially based on Siberian Shamans consuming psychedelic mushrooms with reindeer.1 1https://www.npr.org/2010/12/24/132260025/did-shrooms-send-santa- -
Optimal Dynamic Treatment Regimes and Partial Welfare Ordering∗
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 -
Copyright by Susan Laura Murphy 2019
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. -
Investigation of 2 Types of Self-Administered Acupressure for Persistent Cancer-Related Fatigue in Breast Cancer Survivors
1 ACUPRESSURE FOR PERSISTENT CANCER RELATED FATIGUE 2 3 Principal Investigator: 4 Suzanna M. Zick, ND, MPH 5 24 Frank Lloyd Wright Drive 6 P.O. Box 375 Lobby M 7 Ann Arbor, MI 48106 8 Ph: 734-998-9553 9 Fax 734-998-6900 10 11 Co-Principal Investigator: 12 Richard E. Harris, PhD 13 24 Frank Lloyd Wright Drive 14 P.O. Box 375 Lobby M 15 Ann Arbor, MI 48106 16 Ph: 734-998-6996 17 Fax 734-998-6900 18 19 Co-Investigators: 20 Ananda Sen, PhD 21 J. Todd Arnedt, PhD 22 Susan Murphy, ScD, OTR 23 Gwen Wyatt, PhD, MSN, RN, FAAN 24 Brad Foerster, MD 25 26 Study Coordinators: 27 Vinita Verma / Kevin Shrestha 28 Ph: 734-998-0016 29 Fax: 734-998-6900 30 31 Project Manager 32 Benjamin Wright 33 Ph: 734-998-0031 34 Fax: 734-998-6900 35 36 University of Michigan Health Systems, Ann Arbor, MI 37 Approved by UM IRBMED: HUM00038428 38 39 Current protocol submission and version number: 40 Version 12 6/2/2014 41 42 Date of previous submission dates and version numbers: 43 Version 11 4/3/2014 44 Version 10 6/14/2013 45 Version 9 11/29/2012 46 Version 8 9/25/2012 47 Version 7 3/28/2012 48 Version 6 10/19/2011 49 Version 5 5/20/2011 50 Version 4 01/26/2011 51 Version 3 10/11/2010 52 Version 2 8/25/2010 53 Version 1 3/12/2010 Downloaded From: https://jamanetwork.com/ on 09/24/2021 Acupressure for Persistent Cancer Related Fatigue Study Protocol: Version 12 54 55 STUDY SCHEMA 56 57 ACUPRESSURE FOR PERSISTENT CANCER RELATED FATIGUE 58 59 PI: Suzanna M. -
Dynamic Treatment Regimens
Dynamic Treatment Regimens Bibhas Chakraborty Centre for Quantitative Medicine, Duke-NUS Graduate Medical School, Singapore [email protected] Innovation in Clinical Trial Methodology Workshop Murdoch Childrens Research Institute Melbourne May 20, 2015 1 / 45 Personalized Medicine Believed by many as the future of medicine ... Source: http://www.personalizedmedicine.com/ Often refers to tailoring by genetic profile, but it’s also common to personalize based on more “macro” level characteristics, some of which are time-varying Personalized Medicine Paradigm shift from “one size fits all” to individualized, patient-centric care – Can address inherent heterogeneity across patients – Can also address variability within patient, over time – Can increase patient compliance, thus increasing the chance of treatment success – Likely to reduce the overall cost of health care 3 / 45 Personalized Medicine: Big Methodological Questions – How to decide on the optimal treatment for an individual patient? – How to make these treatment decisions evidence-based or data-driven? This is where Statistics can make a difference! 4 / 45 Outline 1 Dynamic Treatment Regimens (Regimes): What and Why? 2 Sequential Multiple Assignment Randomized Trial (SMART) Design SMART Design: What and Why? SMART Design Principles, Primary Hypothesis and Sample Size 3 Secondary Analysis of SMART: Finding Optimal DTR 4 Discussion 5 / 45 Dynamic Treatment Regimens (Regimes): What and Why? Dynamic Treatment Regimens Consider personalized management of chronic disorders -
Snehalata Huzurbazar Joins SAMSI As Deputy Director
Volume 5 o nf .i .info Issue 1 Spring 2012 e Institut Sciences Mathematical Applied and Statistical the of Newsletter The The Newsletter of the Statistical and Applied Mathematical Sciences Institute Snehalata Huzurbazar Joins SAMSI as Deputy Director Snehalata Huzurbazar has accepted the position of deputy director of SAMSI, starting July 9. She will be at SAMSI for two years, while taking a leave of absence from the University of Wyoming. Snehalata is replacing Pierre Gremaud, who is finish- ing his fourth year at SAMSI. In her new position, Snehalata will help administer the SAMSI programs and will help to develop future programs. She will also be involved with the education and outreach efforts and will work on staff and personnel issues. Huzurbazar will be a part of the directorate, which comprises the director, three part-time associate directors and the deputy director. Many people may remember Snehalata who was at SAMSI last spring when she was participating in the Analysis of Object- Oriented Data (AOD) program. Her infectious laugh and beauti- ful smile brightened up the hallways of SAMSI. She was very in- volved with the Trees and Dynamics working groups from AOD. Snehalata said she comes from a statistics family. “My father (V. S. Huzurbazar) was a statistician, and Harold Jeffreys’ only statistics student, who started the Mathematics and Statistics Department at the University of Poona in India,” she noted. He Snehalata and her daughter, Asha, taking a hike. was a Fulbright scholar visiting professor at Iowa State when she was born. Her sister, Aparna Huzurbazar is a statistician at Los Institute of Arctic and Alpine Research in Boulder, Colorado.