Evidence from randomized controlled clinical trials is widely accepted as the only sound basis for assessing the efficacy of new medical treatments. Statistical methods play a key role in all stages of these trials, including their justification, design, and analysis. This second edition of Introduction to Randomized Controlled Clinical Trials prov
Bradford Hill has defined a clinical trial as "A carefully and ethically designed experiment with the aim of answering some precisely framed question" [1]. This definition specifies a careful design and requires the provision of adequate controls. Random allocation of treatments to subjects is important to ensure is entitled that the treated and control groups are similar. Therefore this book Randomised Controlled Clinical Trials. We can define a randomised controlled trial by rewriting Bradford Hill's definition as follows, "A carefully and ethi cally designed experiment which includes the provision of adequate and ap propriate controls by a process of randomisation, so that precisely framed questions can be answered. " I am a firm advocate ofRandomised Controlled Clinical Trials but intend to give a balanced view of the advantages and disadvantages of these ethical experiments. This book is directed primarily at the medical research worker, although certain chapters may find a wider application. When discussing a randomised controlled trial, it is neither practicable nor desirable to divorce theory from practice, however the first ten chapters con centrate mainly on theory, and the remainder focus on practice. The segment on trial design is followed by sections on writing the protocol, designing the forms, conducting the trial, and analysing the results. This book is meant to serve both as a reference manual and a practical guide to the design and performance of a trial.
Randomized controlled trials are one of the most powerful and revolutionary tools of research. This book is a convenient and accessible description of the underlying principles and practice of randomized controlled trials and their role in clinical decision-making. Structured in a jargon-free question-and-answer format, each chapter provides concise and understandable information on a different aspect of randomized controlled trials, from the basics of trial design and terminology to the interpretation of results and their use in driving evidence-based medicine. The authors end each chapter with their musings, going beyond the evidence or citations, and sometimes even beyond orthodox correctness to share their thoughts and concerns about different aspects of randomized controlled trials, and their role within the health system. Updated to include insights from the last decade, this second edition challenges over-reliance on randomized controlled trials by debating their strengths and limitations and discussing their optimal use in modern healthcare. It also includes a new and increasingly relevant chapter on the ethics of randomized trials. World renowned writers and thinkers Drs Jadad and Enkin bring you this invaluable book for busy health professionals who wish to understand the theory of randomized controlled trials and their influence on clinical, research or policy decisions.
Clinical trials are used to elucidate the most appropriate preventive, diagnostic, or treatment options for individuals with a given medical condition. Perhaps the most essential feature of a clinical trial is that it aims to use results based on a limited sample of research participants to see if the intervention is safe and effective or if it is comparable to a comparison treatment. Sample size is a crucial component of any clinical trial. A trial with a small number of research participants is more prone to variability and carries a considerable risk of failing to demonstrate the effectiveness of a given intervention when one really is present. This may occur in phase I (safety and pharmacologic profiles), II (pilot efficacy evaluation), and III (extensive assessment of safety and efficacy) trials. Although phase I and II studies may have smaller sample sizes, they usually have adequate statistical power, which is the committee's definition of a "large" trial. Sometimes a trial with eight participants may have adequate statistical power, statistical power being the probability of rejecting the null hypothesis when the hypothesis is false. Small Clinical Trials assesses the current methodologies and the appropriate situations for the conduct of clinical trials with small sample sizes. This report assesses the published literature on various strategies such as (1) meta-analysis to combine disparate information from several studies including Bayesian techniques as in the confidence profile method and (2) other alternatives such as assessing therapeutic results in a single treated population (e.g., astronauts) by sequentially measuring whether the intervention is falling above or below a preestablished probability outcome range and meeting predesigned specifications as opposed to incremental improvement.
Randomized clinical trials are the primary tool for evaluating new medical interventions. Randomization provides for a fair comparison between treatment and control groups, balancing out, on average, distributions of known and unknown factors among the participants. Unfortunately, these studies often lack a substantial percentage of data. This missing data reduces the benefit provided by the randomization and introduces potential biases in the comparison of the treatment groups. Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation. And in some studies, some or all of data collection ceases when participants discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable. The Prevention and Treatment of Missing Data in Clinical Trials concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations, the book offers more detailed recommendations on the conduct of clinical trials and techniques for analysis of trial data.
This is an open access title available under the terms of a CC BY-NC 4.0 International licence. It is free to read at Oxford Scholarship Online and offered as a free PDF download from OUP and selected open access locations. Before new interventions are released into disease control programmes, it is essential that they are carefully evaluated in field trials'. These may be complex and expensive undertakings, requiring the follow-up of hundreds, or thousands, of individuals, often for long periods. Descriptions of the detailed procedures and methods used in the trials that have been conducted have rarely been published. A consequence of this, individuals planning such trials have few guidelines available and little access to knowledge accumulated previously, other than their own. In this manual, practical issues in trial design and conduct are discussed fully and in sufficient detail, that Field Trials of Health Interventions may be used as a toolbox' by field investigators. It has been compiled by an international group of over 30 authors with direct experience in the design, conduct, and analysis of field trials in low and middle income countries and is based on their accumulated knowledge and experience. Available as an open access book via Oxford Medicine Online, this new edition is a comprehensive revision, incorporating the new developments that have taken place in recent years with respect to trials, including seven new chapters on subjects ranging from trial governance, and preliminary studies to pilot testing.
Praise for the First Edition “All medical statisticians involved in clinical trials should read this book...” - Controlled Clinical Trials Featuring a unique combination of the applied aspects of randomization in clinical trials with a nonparametric approach to inference, Randomization in Clinical Trials: Theory and Practice, Second Edition is the go-to guide for biostatisticians and pharmaceutical industry statisticians. Randomization in Clinical Trials: Theory and Practice, Second Edition features: Discussions on current philosophies, controversies, and new developments in the increasingly important role of randomization techniques in clinical trials A new chapter on covariate-adaptive randomization, including minimization techniques and inference New developments in restricted randomization and an increased focus on computation of randomization tests as opposed to the asymptotic theory of randomization tests Plenty of problem sets, theoretical exercises, and short computer simulations using SAS® to facilitate classroom teaching, simplify the mathematics, and ease readers’ understanding Randomization in Clinical Trials: Theory and Practice, Second Edition is an excellent reference for researchers as well as applied statisticians and biostatisticians. The Second Edition is also an ideal textbook for upper-undergraduate and graduate-level courses in biostatistics and applied statistics. William F. Rosenberger, PhD, is University Professor and Chairman of the Department of Statistics at George Mason University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and author of over 80 refereed journal articles, as well as The Theory of Response-Adaptive Randomization in Clinical Trials, also published by Wiley. John M. Lachin, ScD, is Research Professor in the Department of Epidemiology and Biostatistics as well as in the Department of Statistics at The George Washington University. A Fellow of the American Statistical Association and the Society for Clinical Trials, Dr. Lachin is actively involved in coordinating center activities for clinical trials of diabetes. He is the author of Biostatistical Methods: The Assessment of Relative Risks, Second Edition, also published by Wiley.
Covering all commonly used interventions for acute and chronic low back pain conditions, Evidence-Based Management of Low Back Pain consolidates current scientific studies and research evidence into a single, practical resource. Its multidisciplinary approach covers a wide scope of treatments from manual therapies to medical interventions to surgery, organizing interventions from least to most invasive. Editors Simon Dagenais and Scott Haldeman, along with expert contributors from a variety of clinical and academic institutions throughout the world, focus on the best available scientific evidence, summarizing the results from the strongest to the weakest types of studies. No other book makes it so easy to compare the different interventions and treatment approaches, giving you the tools to make better, more informed clinical decisions. - A multidisciplinary approach covers treatments from manual therapies to medical interventions to surgery, and many others in between. - An interdisciplinary approach enables health care providers to work together. - A logical, easy-to-follow organization covers information by intervention type, from least invasive to most invasive. - Integration of interventions provides information in a clinically useful way, so it's easier to consider more than one type of treatment or intervention for low back pain, and easier to see which methods should be tried first. - 155 illustrations include x-rays, photos, and drawings. - Tables and boxes summarize key information. - Evidence-based content allows you to make clinical decisions based on the ranking the best available scientific studies from strongest to weakest. - Patient history and examination chapters help in assessing the patient's condition and in ruling out serious pathology before making decisions about specific interventions. - Experienced editors and contributors are proven authors, researchers, and teachers, and practitioners, well known in the areas of orthopedics, pain management, chiropractic, physical therapy, and behavioral medicine as well as complementary and alternative medicine; the book's contributors include some of the leading clinical and research experts in the field of low back pain. - Coverage based on The Spine Journal special issue on low back pain ensures that topics are relevant and up to date. - A systematic review of interventions for low back pain includes these categories: patient education, exercise and rehabilitation, medications, manual therapy, physical modalities, complementary and alternative medicine, behavioral modification, injections, minimally invasive procedures, and surgery. - Surgical interventions include decompression, fusion, disc arthroplasty, and dynamic stabilization. - Additional coverage includes patient education and multidisciplinary rehabilitation.
Over the last twenty years there has been a dramatic upsurge in the application of meta-analysis to medical research. This has mainly been due to greater emphasis on evidence-based medicine and the need for reliable summaries of the vast and expanding volume of clinical research. At the same time there have been great strides in the development and refinement of the associated statistical methodology. This book describes the planning, conduct and reporting of a meta-analysis as applied to a series of randomized controlled clinical trials. * The various approaches are presented within a general unified framework. * Meta-analysis techniques are described in detail, from their theoretical development through to practical implementation. * Each topic discussed is supported by detailed worked examples. * A comparison of fixed and random effects approaches is included, as well as a discussion of Bayesian methods and cumulative meta-analysis. * Fully documented programs using standard statistical procedures in SAS are available on the Web. Ideally suited for practising statisticians and statistically-minded medical professionals, the book will also be of use to graduate students of medical statistics. The book is a self-contained and comprehensive account of the subject and an essential purchase for anyone involved in clinical trials.
The internal validity of a study reflects the extent to which the design and conduct of the study have prevented bias(es). One of the key steps in a systematic review is assessment of a study's internal validity, or potential for bias. This assessment serves to: (1) identify the strengths and limitations of the included studies; (2) investigate, and potentially explain heterogeneity in findings across different studies included in a systematic review; and (3) grade the strength of evidence for a given question. The risk of bias assessment directly informs one of four key domains considered when assessing the strength of evidence. With the increase in the number of published systematic reviews and development of systematic review methodology over the past 15 years, close attention has been paid to the methods for assessing internal validity. Until recently this has been referred to as “quality assessment” or “assessment of methodological quality.” In this context “quality” refers to “the confidence that the trial design, conduct, and analysis has minimized or avoided biases in its treatment comparisons.” To facilitate the assessment of methodological quality, a plethora of tools has emerged. Some of these tools were developed for specific study designs (e.g., randomized controlled trials (RCTs), cohort studies, case-control studies), while others were intended to be applied to a range of designs. The tools often incorporate characteristics that may be associated with bias; however, many tools also contain elements related to reporting (e.g., was the study population described) and design (e.g., was a sample size calculation performed) that are not related to bias. The Cochrane Collaboration recently developed a tool to assess the potential risk of bias in RCTs. The Risk of Bias (ROB) tool was developed to address some of the shortcomings of existing quality assessment instruments, including over-reliance on reporting rather than methods. Several systematic reviews have catalogued and critiqued the numerous tools available to assess methodological quality, or risk of bias of primary studies. In summary, few existing tools have undergone extensive inter-rater reliability or validity testing. Moreover, the focus of much of the tool development or testing that has been done has been on criterion or face validity. Therefore it is unknown whether, or to what extent, the summary assessments based on these tools differentiate between studies with biased and unbiased results (i.e., studies that may over- or underestimate treatment effects). There is a clear need for inter-rater reliability testing of different tools in order to enhance consistency in their application and interpretation across different systematic reviews. Further, validity testing is essential to ensure that the tools being used can identify studies with biased results. Finally, there is a need to determine inter-rater reliability and validity in order to support the uptake and use of individual tools that are recommended by the systematic review community, and specifically the ROB tool within the Evidence-based Practice Center (EPC) Program. In this project we focused on two tools that are commonly used in systematic reviews. The Cochrane ROB tool was designed for RCTs and is the instrument recommended by The Cochrane Collaboration for use in systematic reviews of RCTs. The Newcastle-Ottawa Scale is commonly used for nonrandomized studies, specifically cohort and case-control studies.
