Optimal Adaptive Designs and Adaptive Randomization Techniques for Clinical Trials
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Design of Experiments Application, Concepts, Examples: State of the Art
Periodicals of Engineering and Natural Scinces ISSN 2303-4521 Vol 5, No 3, December 2017, pp. 421‒439 Available online at: http://pen.ius.edu.ba Design of Experiments Application, Concepts, Examples: State of the Art Benjamin Durakovic Industrial Engineering, International University of Sarajevo Article Info ABSTRACT Article history: Design of Experiments (DOE) is statistical tool deployed in various types of th system, process and product design, development and optimization. It is Received Aug 3 , 2018 multipurpose tool that can be used in various situations such as design for th Revised Oct 20 , 2018 comparisons, variable screening, transfer function identification, optimization th Accepted Dec 1 , 2018 and robust design. This paper explores historical aspects of DOE and provides state of the art of its application, guides researchers how to conceptualize, plan and conduct experiments, and how to analyze and interpret data including Keyword: examples. Also, this paper reveals that in past 20 years application of DOE have Design of Experiments, been grooving rapidly in manufacturing as well as non-manufacturing full factorial design; industries. It was most popular tool in scientific areas of medicine, engineering, fractional factorial design; biochemistry, physics, computer science and counts about 50% of its product design; applications compared to all other scientific areas. quality improvement; Corresponding Author: Benjamin Durakovic International University of Sarajevo Hrasnicka cesta 15 7100 Sarajevo, Bosnia Email: [email protected] 1. Introduction Design of Experiments (DOE) mathematical methodology used for planning and conducting experiments as well as analyzing and interpreting data obtained from the experiments. It is a branch of applied statistics that is used for conducting scientific studies of a system, process or product in which input variables (Xs) were manipulated to investigate its effects on measured response variable (Y). -
How Many Stratification Factors Is Too Many" to Use in a Randomization
How many Strati cation Factors is \Too Many" to Use in a Randomization Plan? Terry M. Therneau, PhD Abstract The issue of strati cation and its role in patient assignment has gen- erated much discussion, mostly fo cused on its imp ortance to a study [1,2] or lack thereof [3,4]. This rep ort fo cuses on a much narrower prob- lem: assuming that strati ed assignment is desired, how many factors can b e accomo dated? This is investigated for two metho ds of balanced patient assignment, the rst is based on the minimization metho d of Taves [5] and the second on the commonly used metho d of strati ed assignment [6,7]. Simulation results show that the former metho d can accomo date a large number of factors 10-20 without diculty, but that the latter b egins to fail if the total numb er of distinct combinations of factor levels is greater than approximately n=2. The two metho ds are related to a linear discriminant mo del, which helps to explain the results. 1 1 Intro duction This work arose out of my involvement with lung cancer studies within the North Central Cancer Treatment Group. These studies are small, typically 100 to 200 patients, and there are several imp ortant prognostic factors on whichwe wish to balance. Some of the factors, such as p erformance or Karnofsky score, are likely to contribute a larger survival impact than the treatment under study; hazards ratios of 3{4 are not uncommon. In this setting it b ecomes very dicult to interpret study results if any of the factors are signi cantly out of balance. -
Design of Experiments (DOE) Using JMP Charles E
PharmaSUG 2014 - Paper PO08 Design of Experiments (DOE) Using JMP Charles E. Shipp, Consider Consulting, Los Angeles, CA ABSTRACT JMP has provided some of the best design of experiment software for years. The JMP team continues the tradition of providing state-of-the-art DOE support. In addition to the full range of classical and modern design of experiment approaches, JMP provides a template for Custom Design for specific requirements. The other choices include: Screening Design; Response Surface Design; Choice Design; Accelerated Life Test Design; Nonlinear Design; Space Filling Design; Full Factorial Design; Taguchi Arrays; Mixture Design; and Augmented Design. Further, sample size and power plots are available. We give an introduction to these methods followed by a few examples with factors. BRIEF HISTORY AND BACKGROUND From early times, there has been a desire to improve processes with controlled experimentation. A famous early experiment cured scurvy with seamen taking citrus fruit. Lives were saved. Thomas Edison invented the light bulb with many experiments. These experiments depended on trial and error with no software to guide, measure, and refine experimentation. Japan led the way with improving manufacturing—Genichi Taguchi wanted to create repeatable and robust quality in manufacturing. He had been taught management and statistical methods by William Edwards Deming. Deming taught “Plan, Do, Check, and Act”: PLAN (design) the experiment; DO the experiment by performing the steps; CHECK the results by testing information; and ACT on the decisions based on those results. In America, other pioneers brought us “Total Quality” and “Six Sigma” with “Design of Experiments” being an advanced methodology. -
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Harmonizing Fully Optimal Designs with Classic Randomization in Fixed Trial Experiments Arxiv:1810.08389V1 [Stat.ME] 19 Oct 20
Harmonizing Fully Optimal Designs with Classic Randomization in Fixed Trial Experiments Adam Kapelner, Department of Mathematics, Queens College, CUNY, Abba M. Krieger, Department of Statistics, The Wharton School of the University of Pennsylvania, Uri Shalit and David Azriel Faculty of Industrial Engineering and Management, The Technion October 22, 2018 Abstract There is a movement in design of experiments away from the classic randomization put forward by Fisher, Cochran and others to one based on optimization. In fixed-sample trials comparing two groups, measurements of subjects are known in advance and subjects can be divided optimally into two groups based on a criterion of homogeneity or \imbalance" between the two groups. These designs are far from random. This paper seeks to understand the benefits and the costs over classic randomization in the context of different performance criterions such as Efron's worst-case analysis. In the criterion that we motivate, randomization beats optimization. However, the optimal arXiv:1810.08389v1 [stat.ME] 19 Oct 2018 design is shown to lie between these two extremes. Much-needed further work will provide a procedure to find this optimal designs in different scenarios in practice. Until then, it is best to randomize. Keywords: randomization, experimental design, optimization, restricted randomization 1 1 Introduction In this short survey, we wish to investigate performance differences between completely random experimental designs and non-random designs that optimize for observed covariate imbalance. We demonstrate that depending on how we wish to evaluate our estimator, the optimal strategy will change. We motivate a performance criterion that when applied, does not crown either as the better choice, but a design that is a harmony between the two of them. -
Some Restricted Randomization Rules in Sequential Designs Jose F
This article was downloaded by: [Georgia Tech Library] On: 10 April 2013, At: 12:48 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Communications in Statistics - Theory and Methods Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/lsta20 Some Restricted randomization rules in sequential designs Jose F. Soares a & C.F. Jeff Wu b a Federal University of Minas Gerais, Brazil b University of Wisconsin Madison and Mathematical Sciences Research Institute, Berkeley Version of record first published: 27 Jun 2007. To cite this article: Jose F. Soares & C.F. Jeff Wu (1983): Some Restricted randomization rules in sequential designs, Communications in Statistics - Theory and Methods, 12:17, 2017-2034 To link to this article: http://dx.doi.org/10.1080/03610928308828586 PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. -
Optimal Design of Experiments Session 4: Some Theory
Optimal design of experiments Session 4: Some theory Peter Goos 1 / 40 Optimal design theory Ï continuous or approximate optimal designs Ï implicitly assume an infinitely large number of observations are available Ï is mathematically convenient Ï exact or discrete designs Ï finite number of observations Ï fewer theoretical results 2 / 40 Continuous versus exact designs continuous Ï ½ ¾ x1 x2 ... xh Ï » Æ w1 w2 ... wh Ï x1,x2,...,xh: design points or support points w ,w ,...,w : weights (w 0, P w 1) Ï 1 2 h i ¸ i i Æ Ï h: number of different points exact Ï ½ ¾ x1 x2 ... xh Ï » Æ n1 n2 ... nh Ï n1,n2,...,nh: (integer) numbers of observations at x1,...,xn P n n Ï i i Æ Ï h: number of different points 3 / 40 Information matrix Ï all criteria to select a design are based on information matrix Ï model matrix 2 3 2 T 3 1 1 1 1 1 1 f (x1) ¡ ¡ Å Å Å T 61 1 1 1 1 17 6f (x2)7 6 Å ¡ ¡ Å Å 7 6 T 7 X 61 1 1 1 1 17 6f (x3)7 6 ¡ Å ¡ Å Å 7 6 7 Æ 6 . 7 Æ 6 . 7 4 . 5 4 . 5 T 100000 f (xn) " " " " "2 "2 I x1 x2 x1x2 x1 x2 4 / 40 Information matrix Ï (total) information matrix 1 1 Xn M XT X f(x )fT (x ) 2 2 i i Æ σ Æ σ i 1 Æ Ï per observation information matrix 1 T f(xi)f (xi) σ2 5 / 40 Information matrix industrial example 2 3 11 0 0 0 6 6 6 0 6 0 0 0 07 6 7 1 6 0 0 6 0 0 07 ¡XT X¢ 6 7 2 6 7 σ Æ 6 0 0 0 4 0 07 6 7 4 6 0 0 0 6 45 6 0 0 0 4 6 6 / 40 Information matrix Ï exact designs h X T M ni f(xi)f (xi) Æ i 1 Æ where h = number of different points ni = number of replications of point i Ï continuous designs h X T M wi f(xi)f (xi) Æ i 1 Æ 7 / 40 D-optimality criterion Ï seeks designs that minimize variance-covariance matrix of ¯ˆ ¯ 2 T 1¯ Ï . -
Clinical Performance Assessment: Considerations for Computer
Contains Nonbinding Recommendations Clinical Performance Assessment: Considerations for Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data in - Premarket Notification (510(k)) Submissions Guidance for Industry and FDA Staff Document issued on: January 22, 2020 Document originally issued on July 3, 2012 For questions regarding this guidance document, contact Nicholas Petrick at 301-796-2563, or by e- mail at [email protected]; or Robert Ochs at 301-796-6661, or by e-mail at [email protected]. U.S. Department of Health and Human Services Food and Drug Administration Center for Devices and Radiological Health Contains Nonbinding Recommendations Preface Public Comment You may submit electronic comments and suggestions at any time for Agency consideration to https://www.regulations.gov. Submit written comments to the Dockets Management Staff, Food and Drug Administration, 5630 Fishers Lane, Room 1061, (HFA-305), Rockville, MD 20852. Identify all comments with the docket number FDA-2009-D-0503. Comments may not be acted upon by the Agency until the document is next revised or updated. Additional Copies Additional copies are available from the Internet. You may also send an e-mail request to CDRH- [email protected] to receive a copy of the guidance. Please use the document number (1698) to identify the guidance you are requesting. Table of Contents 1. INTRODUCTION .................................................................................................................... 4 2. -
FDA Guidance: “Design Considerations for Pivotal Clinical Investigations for Medical Devices”
FDA Guidance: “Design Considerations for Pivotal Clinical Investigations for Medical Devices” Greg Campbell, Ph.D. Director, Division of Biostatistics Center for Devices and Radiological Health U.S. Food and Drug Administration [email protected] FDA Stakeholder Webinar December 4, 2013 Design Considerations for Pivotal Clinical Investigations • http://www.fda.gov/medicaldevices/deviceregulation andguidance/guidancedocuments/ucm373750.htm • Draft issued Aug. 15, 2011; Final issued Nov. 7, 2013. • It is guidance for industry, clinical investigators, institutional review boards and FDA staff. 2 Design Considerations for Pivotal Clinical Investigations • Guidance should help manufacturers select appropriate trial design. • Better trial design and improve the quality of data that may better support the safety and effectiveness of a device. • Better quality data may lead to timelier FDA approval or clearance of premarket submissions and speed U.S. patient access to new devices. 3 Outline • Regulatory Considerations • Principles for the Choice of Clinical Study Design • Clinical Outcome Studies – RCTs – Controls – Non-Randomized Controls – Observational Uncontrolled One-Arm Studies • OPCs and Performance Goals – Diagnostic Clinical Outcome Studies • Diagnostic Clinical Performance Studies • Sustaining the Level of Evidence of Studies • Protocol and Statistical Analysis Plan • Closing Remarks 4 Stages of Medical Device Clinical Studies 1) Exploratory Stage – first-in-human and feasibility/pilot studies, iterative learning and product development. 2) Pivotal Stage – definitive study to support the safety and effectiveness evaluation of the medical device for its intended use. 3) Postmarket Stage – includes studies intended to better understand the long-term effectiveness and safety of the device, including rare adverse events. Not all products will go through all stages. -
Concepts Underlying the Design of Experiments
Concepts Underlying the Design of Experiments March 2000 University of Reading Statistical Services Centre Biometrics Advisory and Support Service to DFID Contents 1. Introduction 3 2. Specifying the objectives 5 3. Selection of treatments 6 3.1 Terminology 6 3.2 Control treatments 7 3.3 Factorial treatment structure 7 4. Choosing the sites 9 5. Replication and levels of variation 10 6. Choosing the blocks 12 7. Size and shape of plots in field experiments 13 8. Allocating treatment to units 14 9. Taking measurements 15 10. Data management and analysis 17 11. Taking design seriously 17 © 2000 Statistical Services Centre, The University of Reading, UK 1. Introduction This guide describes the main concepts that are involved in designing an experiment. Its direct use is to assist scientists involved in the design of on-station field trials, but many of the concepts also apply to the design of other research exercises. These range from simulation exercises, and laboratory studies, to on-farm experiments, participatory studies and surveys. What characterises the design of on-station experiments is that the researcher has control of the treatments to apply and the units (plots) to which they will be applied. The results therefore usually provide a detailed knowledge of the effects of the treat- ments within the experiment. The major limitation is that they provide this information within the artificial "environment" of small plots in a research station. A laboratory study should achieve more precision, but in a yet more artificial environment. Even more precise is a simulation model, partly because it is then very cheap to collect data. -
The Theory of the Design of Experiments
The Theory of the Design of Experiments D.R. COX Honorary Fellow Nuffield College Oxford, UK AND N. REID Professor of Statistics University of Toronto, Canada CHAPMAN & HALL/CRC Boca Raton London New York Washington, D.C. C195X/disclaimer Page 1 Friday, April 28, 2000 10:59 AM Library of Congress Cataloging-in-Publication Data Cox, D. R. (David Roxbee) The theory of the design of experiments / D. R. Cox, N. Reid. p. cm. — (Monographs on statistics and applied probability ; 86) Includes bibliographical references and index. ISBN 1-58488-195-X (alk. paper) 1. Experimental design. I. Reid, N. II.Title. III. Series. QA279 .C73 2000 001.4 '34 —dc21 00-029529 CIP This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage or retrieval system, without prior permission in writing from the publisher. The consent of CRC Press LLC does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from CRC Press LLC for such copying. Direct all inquiries to CRC Press LLC, 2000 N.W. -
Argus: Interactive a Priori Power Analysis
© 2020 IEEE. This is the author’s version of the article that has been published in IEEE Transactions on Visualization and Computer Graphics. The final version of this record is available at: xx.xxxx/TVCG.201x.xxxxxxx/ Argus: Interactive a priori Power Analysis Xiaoyi Wang, Alexander Eiselmayer, Wendy E. Mackay, Kasper Hornbæk, Chat Wacharamanotham 50 Reading Time (Minutes) Replications: 3 Power history Power of the hypothesis Screen – Paper 1.0 40 1.0 Replications: 2 0.8 0.8 30 Pairwise differences 0.6 0.6 20 0.4 0.4 10 0 1 2 3 4 5 6 One_Column Two_Column One_Column Two_Column 0.2 Minutes with 95% confidence interval 0.2 Screen Paper Fatigue Effect Minutes 0.0 0.0 0 2 3 4 6 8 10 12 14 8 12 16 20 24 28 32 36 40 44 48 Fig. 1: Argus interface: (A) Expected-averages view helps users estimate the means of the dependent variables through interactive chart. (B) Confound sliders incorporate potential confounds, e.g., fatigue or practice effects. (C) Power trade-off view simulates data to calculate statistical power; and (D) Pairwise-difference view displays confidence intervals for mean differences, animated as a dance of intervals. (E) History view displays an interactive power history tree so users can quickly compare statistical power with previously explored configurations. Abstract— A key challenge HCI researchers face when designing a controlled experiment is choosing the appropriate number of participants, or sample size. A priori power analysis examines the relationships among multiple parameters, including the complexity associated with human participants, e.g., order and fatigue effects, to calculate the statistical power of a given experiment design. -
Advanced Power Analysis Workshop
Advanced Power Analysis Workshop www.nicebread.de PD Dr. Felix Schönbrodt & Dr. Stella Bollmann www.researchtransparency.org Ludwig-Maximilians-Universität München Twitter: @nicebread303 •Part I: General concepts of power analysis •Part II: Hands-on: Repeated measures ANOVA and multiple regression •Part III: Power analysis in multilevel models •Part IV: Tailored design analyses by simulations in 2 Part I: General concepts of power analysis •What is “statistical power”? •Why power is important •From power analysis to design analysis: Planning for precision (and other stuff) •How to determine the expected/minimally interesting effect size 3 What is statistical power? A 2x2 classification matrix Reality: Reality: Effect present No effect present Test indicates: True Positive False Positive Effect present Test indicates: False Negative True Negative No effect present 5 https://effectsizefaq.files.wordpress.com/2010/05/type-i-and-type-ii-errors.jpg 6 https://effectsizefaq.files.wordpress.com/2010/05/type-i-and-type-ii-errors.jpg 7 A priori power analysis: We assume that the effect exists in reality Reality: Reality: Effect present No effect present Power = α=5% Test indicates: 1- β p < .05 True Positive False Positive Test indicates: β=20% p > .05 False Negative True Negative 8 Calibrate your power feeling total n Two-sample t test (between design), d = 0.5 128 (64 each group) One-sample t test (within design), d = 0.5 34 Correlation: r = .21 173 Difference between two correlations, 428 r₁ = .15, r₂ = .40 ➙ q = 0.273 ANOVA, 2x2 Design: Interaction effect, f = 0.21 180 (45 each group) All a priori power analyses with α = 5%, β = 20% and two-tailed 9 total n Two-sample t test (between design), d = 0.5 128 (64 each group) One-sample t test (within design), d = 0.5 34 Correlation: r = .21 173 Difference between two correlations, 428 r₁ = .15, r₂ = .40 ➙ q = 0.273 ANOVA, 2x2 Design: Interaction effect, f = 0.21 180 (45 each group) All a priori power analyses with α = 5%, β = 20% and two-tailed 10 The power ofwithin-SS designs Thepower 11 May, K., & Hittner, J.