Randomized Controlled Trial
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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. -
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. -
Survey Experiments
IU Workshop in Methods – 2019 Survey Experiments Testing Causality in Diverse Samples Trenton D. Mize Department of Sociology & Advanced Methodologies (AMAP) Purdue University Survey Experiments Page 1 Survey Experiments Page 2 Contents INTRODUCTION ............................................................................................................................................................................ 8 Overview .............................................................................................................................................................................. 8 What is a survey experiment? .................................................................................................................................... 9 What is an experiment?.............................................................................................................................................. 10 Independent and dependent variables ................................................................................................................. 11 Experimental Conditions ............................................................................................................................................. 12 WHY CONDUCT A SURVEY EXPERIMENT? ........................................................................................................................... 13 Internal, external, and construct validity .......................................................................................................... -
Analysis of Variance and Analysis of Variance and Design of Experiments of Experiments-I
Analysis of Variance and Design of Experimentseriments--II MODULE ––IVIV LECTURE - 19 EXPERIMENTAL DESIGNS AND THEIR ANALYSIS Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur 2 Design of experiment means how to design an experiment in the sense that how the observations or measurements should be obtained to answer a qqyuery inavalid, efficient and economical way. The desigggning of experiment and the analysis of obtained data are inseparable. If the experiment is designed properly keeping in mind the question, then the data generated is valid and proper analysis of data provides the valid statistical inferences. If the experiment is not well designed, the validity of the statistical inferences is questionable and may be invalid. It is important to understand first the basic terminologies used in the experimental design. Experimental unit For conducting an experiment, the experimental material is divided into smaller parts and each part is referred to as experimental unit. The experimental unit is randomly assigned to a treatment. The phrase “randomly assigned” is very important in this definition. Experiment A way of getting an answer to a question which the experimenter wants to know. Treatment Different objects or procedures which are to be compared in an experiment are called treatments. Sampling unit The object that is measured in an experiment is called the sampling unit. This may be different from the experimental unit. 3 Factor A factor is a variable defining a categorization. A factor can be fixed or random in nature. • A factor is termed as fixed factor if all the levels of interest are included in the experiment. -
The Politics of Random Assignment: Implementing Studies and Impacting Policy
The Politics of Random Assignment: Implementing Studies and Impacting Policy Judith M. Gueron Manpower Demonstration Research Corporation (MDRC) As the only nonacademic presenting a paper at this conference, I see it as my charge to focus on the challenge of implementing random assignment in the field. I will not spend time arguing for the methodological strengths of social experiments or advocating for more such field trials. Others have done so eloquently.1 But I will make my biases clear. For 25 years, I and many of my MDRC colleagues have fought to implement random assignment in diverse arenas and to show that this approach is feasible, ethical, uniquely convincing, and superior for answering certain questions. Our organization is widely credited with being one of the pioneers of this approach, and through its use producing results that are trusted across the political spectrum and that have made a powerful difference in social policy and research practice. So, I am a believer, but not, I hope, a blind one. I do not think that random assignment is a panacea or that it can address all the critical policy questions, or substitute for other types of analysis, or is always appropriate. But I do believe that it offers unique power in answering the “Does it make a difference?” question. With random assignment, you can know something with much greater certainty and, as a result, can more confidently separate fact from advocacy. This paper focuses on implementing experiments. In laying out the ingredients of success, I argue that creative and flexible research design skills are essential, but that just as important are operational and political skills, applied both to marketing the experiment in the first place and to helping interpret and promote its findings down the line. -
Key Items to Get Right When Conducting Randomized Controlled Trials of Social Programs
Key Items to Get Right When Conducting Randomized Controlled Trials of Social Programs February 2016 This publication was produced by the Evidence-Based Policy team of the Laura and John Arnold Foundation (now Arnold Ventures). This publication is in the public domain. Authorization to reproduce it in whole or in part for educational purposes is granted. We welcome comments and suggestions on this document ([email protected]). 1 Purpose This is a checklist of key items to get right when conducting a randomized controlled trial (RCT) to evaluate a social program or practice. The checklist is designed to be a practical resource for researchers and sponsors of research. It describes items that are critical to the success of an RCT in producing valid findings about a social program’s effectiveness. This document is limited to key items, and does not address all contingencies that may affect a study’s success.1 Items in this checklist are categorized according to the following phases of an RCT: 1. Planning the study; 2. Carrying out random assignment; 3. Measuring outcomes for the study sample; and 4. Analyzing the study results. 1. Key items to get right in planning the study Choose (i) the program to be evaluated, (ii) target population for the study, and (iii) key outcomes to be measured. These should include, wherever possible, ultimate outcomes of policy importance. As illustrative examples: . An RCT of a pregnancy prevention program preferably should measure outcomes such as pregnancies or births, and not just intermediate outcomes such as condom use. An RCT of a remedial reading program preferably should measure outcomes such as reading comprehension, and not just participants’ ability to sound out words. -
Chapter 5 Experiments, Good And
Chapter 5 Experiments, Good and Bad Point of both observational studies and designed experiments is to identify variable or set of variables, called explanatory variables, which are thought to predict outcome or response variable. Confounding between explanatory variables occurs when two or more explanatory variables are not separated and so it is not clear how much each explanatory variable contributes in prediction of response variable. Lurking variable is explanatory variable not considered in study but confounded with one or more explanatory variables in study. Confounding with lurking variables effectively reduced in randomized comparative experiments where subjects are assigned to treatments at random. Confounding with a (only one at a time) lurking variable reduced in observational studies by controlling for it by comparing matched groups. Consequently, experiments much more effec- tive than observed studies at detecting which explanatory variables cause differences in response. In both cases, statistically significant observed differences in average responses implies differences are \real", did not occur by chance alone. Exercise 5.1 (Experiments, Good and Bad) 1. Randomized comparative experiment: effect of temperature on mice rate of oxy- gen consumption. For example, mice rate of oxygen consumption 10.3 mL/sec when subjected to 10o F. temperature (Fo) 0 10 20 30 ROC (mL/sec) 9.7 10.3 11.2 14.0 (a) Explanatory variable considered in study is (choose one) i. temperature ii. rate of oxygen consumption iii. mice iv. mouse weight 25 26 Chapter 5. Experiments, Good and Bad (ATTENDANCE 3) (b) Response is (choose one) i. temperature ii. rate of oxygen consumption iii. -
Randomized Complete Block Designs
1 2 RANDOMIZED COMPLETE BLOCK DESIGNS Randomization can in principal be used to take into account factors that can be treated by blocking, but Introduction to Blocking blocking usually results in smaller error variance, hence better estimates of effect. Thus blocking is Nuisance factor: A factor that probably has an effect sometimes referred to as a method of variance on the response, but is not a factor that we are reduction design. interested in. The intuitive idea: Run in parallel a bunch of Types of nuisance factors and how to deal with them experiments on groups (called blocks) of units that in designing an experiment: are fairly similar. Characteristics Examples How to treat The simplest block design: The randomized complete Unknown, Experimenter or Randomization block design (RCBD) uncontrollable subject bias, order Blinding of treatments v treatments Known, IQ, weight, Analysis of (They could be treatment combinations.) uncontrollable, previous learning, Covariance measurable temperature b blocks, each with v units Known, Temperature, Blocking Blocks chosen so that units within a block are moderately location, time, alike (or at least similar) and units in controllable (by batch, particular different blocks are substantially different. choosing rather machine or (Thus the total number of experimental units than adjusting) operator, age, is n = bv.) gender, order, IQ, weight The v experimental units within each block are randomly assigned to the v treatments. (So each treatment is assigned one unit per block.) 3 4 Note that experimental units are assigned randomly RCBD Model: only within each block, not overall. Thus this is sometimes called a restricted randomization. -
How to Do Random Allocation (Randomization) Jeehyoung Kim, MD, Wonshik Shin, MD
Special Report Clinics in Orthopedic Surgery 2014;6:103-109 • http://dx.doi.org/10.4055/cios.2014.6.1.103 How to Do Random Allocation (Randomization) Jeehyoung Kim, MD, Wonshik Shin, MD Department of Orthopedic Surgery, Seoul Sacred Heart General Hospital, Seoul, Korea Purpose: To explain the concept and procedure of random allocation as used in a randomized controlled study. Methods: We explain the general concept of random allocation and demonstrate how to perform the procedure easily and how to report it in a paper. Keywords: Random allocation, Simple randomization, Block randomization, Stratified randomization Randomized controlled trials (RCT) are known as the best On the other hand, many researchers are still un- method to prove causality in spite of various limitations. familiar with how to do randomization, and it has been Random allocation is a technique that chooses individuals shown that there are problems in many studies with the for treatment groups and control groups entirely by chance accurate performance of the randomization and that some with no regard to the will of researchers or patients’ con- studies are reporting incorrect results. So, we will intro- dition and preference. This allows researchers to control duce the recommended way of using statistical methods all known and unknown factors that may affect results in for a randomized controlled study and show how to report treatment groups and control groups. the results properly. Allocation concealment is a technique used to pre- vent selection bias by concealing the allocation sequence CATEGORIES OF RANDOMIZATION from those assigning participants to intervention groups, until the moment of assignment. -
A General Overview of Adaptive Randomization Design for Clinical Trials
tri ome cs & Bi B f i o o l s t a a n t r i s u t i o c J s Journal of Biometrics & Biostatistics Lin et al., J Biom Biostat 2016, 7:2 ISSN: 2155-6180 DOI: 10.4172/2155-6180.1000294 Review Article Open Access A General Overview of Adaptive Randomization Design for Clinical Trials Jianchang Lin1*, Li-An Lin2 and Serap Sankoh1 1Takeda Pharmaceutical Company Limited, Cambridge, MA, USA 2Merck Research Laboratories, Whitehouse Station, New Jersey , USA *Corresponding author: Jianchang Lin, Takeda Pharmaceutical Company Limited, Cambridge, MA, USA, Tel: +1-617-679-7000; E-mail: [email protected] Rec date: March 25, 2016; Acc date: April 14, 2016; Pub date: April 21, 2016 Copyright: © 2016 Lin J, et al.This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract With the recent release of FDA draft guidance (2010), adaptive designs, including adaptive randomization (e.g. response-adaptive (RA) randomization) has become popular in clinical trials because of its advantages of flexibility and efficiency gains, which also have the significant ethical advantage of assigning fewer patients to treatment arms with inferior outcomes. In this paper, we presented a general overview of adaptive randomization designs for clinical trials, including Bayesian and frequentist approaches as well as response-adaptive randomization. Examples were used to demonstrate the procedure for design parameters calibration and operating characteristics. Both advantages and disadvantages of adaptive randomization were discussed in the summary from practical perspective of clinical trials. -
Random Assignment
7 RANDOM ASSIGNMENT Just as representativeness can be secured by the method of chance . so equivalence may be secured by chance.1distribute —W. A. McCall or LEARNING OBJECTIVES • Understand what random assignment does and how it works. • Produce a valid randomization processpost, for an experiment and describe it. • Critique simple random assignment, blocking, matched pairs, and stratified random assignment. • Explain the importance of counterbalancing. • Describe a Latincopy, square design. ust as the mantra in real estate is “location, location, location,” the motto in experi- Jmental design is “random assignment, random assignment, random assignment.” This notbook has discussed random assignment all throughout. It bears repeating that ran- dom assignment is the single most important thing a researcher can do in an experiment. Everything else pales in comparison to having done this correctly.2 Random assignment is what distinguishes a true experiment from a quasi, natural, or pre-experimental design. In Dochapter 1, experiments were referred to as the gold standard. Without successful random 173 Copyright ©2019 by SAGE Publications, Inc. This work may not be reproduced or distributed in any form or by any means without express written permission of the publisher. 174 Designing Experiments for the Social Sciences assignment, however, they can quickly become “the bronze standard.”3 This chapter will review some of the advantages of random assignment, discuss the details of how to do it, and explore related issues of counterbalancing. THE PURPOSE OF RANDOM ASSIGNMENT People vary. That is, they are different. Were this not so, there would be no reason to study them. Everyone would be the same, reacting the same way to different teaching techniques, advertisements, health interventions, and political messages.