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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley Series in Probability and Statistics
- Sprache: Englisch
Learn rigorous statistical methods to ensure valid clinicaltrials This Second Edition of the critically hailed Clinical Trials buildson the text's reputation as a straightforward and authoritativepresentation of statistical methods for clinical trials. Readersare introduced to the fundamentals of design for various types ofclinical trials and then skillfully guided through the completeprocess of planning the experiment, assembling a study cohort,assessing data, and reporting results. Throughout the process, theauthor alerts readers to problems that may arise during the courseof the trial and provides commonsense solutions. The author bases the revisions and updates on his own classroomexperience, as well as feedback from students, instructors, andmedical and statistical professionals involved in clinical trials.The Second Edition greatly expands its coverage, ranging fromstatistical principles to controversial topics, includingalternative medicine and ethics. At the same time, it offers morepragmatic advice for issues such as selecting outcomes, samplesize, analysis, reporting, and handling allegations of misconduct.Readers familiar with the First Edition will discover completelynew chapters, including: * Contexts for clinical trials * Statistical perspectives * Translational clinical trials * Dose-finding and dose-ranging designs Each chapter is accompanied by a summary to reinforce the keypoints. Revised discussion questions stimulate critical thinkingand help readers understand how they can apply their newfoundknowledge, and updated references are provided to direct readers tothe most recent literature. This text distinguishes itself with its accessible and broadcoverage of statistical design methods--the crucial building blocksof clinical trials and medical research. Readers learn to conductclinical trials that produce valid qualitative results backed byrigorous statistical methods.
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Seitenzahl: 1455
Veröffentlichungsjahr: 2013
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CONTENTS
PREFACE
PREFACE TO THE FIRST EDITION
1 PRELIMINARIES
1.1 INTRODUCTION
1.2 AUDIENCE AND SCOPE
1.3 OTHER SOURCES OF KNOWLEDGE
1.4 EXAMPLES, DATA, AND PROGRAMS
1.5 SUMMARY
2 CLINICAL TRIALS AS RESEARCH
2.1 INTRODUCTION
2.2 DEFINING CLINICAL TRIALS FORMALLY
2.3 PRACTICALITIES OF USAGE
2.4 SUMMARY
2.5 QUESTIONS FOR DISCUSSION
3 WHY CLINICAL TRIALS ARE ETHICAL
3.1 INTRODUCTION
3.2 DUALITY
3.3 HISTORICALLY DERIVED PRINCIPLES OF ETHICS
3.4 CONTEMPORARY FOUNDATIONAL PRINCIPLES
3.5 METHODOLOGIC REFLECTIONS
3.6 PROFESSIONAL CONDUCT
3.7 SUMMARY
3.8 QUESTIONS FOR DISCUSSION
4 CONTEXTS FOR CLINICAL TRIALS
4.1 INTRODUCTION
4.2 DRUGS
4.3 DEVICES
4.4 PREVENTION
4.5 COMPLEMENTARY AND ALTERNATIVE MEDICINE
4.6 SURGERY AND SKILL-DEPENDENT THERAPIES
4.7 A BRIEF VIEW OF SOME OTHER CONTEXTS
4.8 SUMMARY
4.9 QUESTIONS FOR DISCUSSION
5 STATISTICAL PERSPECTIVES
5.1 INTRODUCTION
5.2 DIFFERENCES IN STATISTICAL PERSPECTIVES
5.3 FREQUENTIST
5.4 BAYESIAN
5.5 LIKELIHOOD
5.6 AFTERTHOUGHTS
5.7 SUMMARY
5.8 QUESTIONS FOR DISCUSSION
6 CLINICAL TRIALS AS EXPERIMENTAL DESIGNS
6.1 INTRODUCTION
6.2 GOALS OF EXPERIMENTAL DESIGN
6.3 TRIAL TERMINOLOGY
6.4 DESIGN CONCEPTS
6.5 SURVEY OF DEVELOPMENTAL TRIAL DESIGNS
6.6 SPECIAL DESIGN ISSUES
6.7 IMPORTANCE OF THE PROTOCOL DOCUMENT
6.8 SUMMARY
6.9 QUESTIONS FOR DISCUSSION
7 RANDOM ERROR AND BIAS
7.1 INTRODUCTION
7.2 RANDOM ERROR
7.3 CLINICAL BIASES
7.4 STATISTICAL BIAS
7.5 SUMMARY
7.6 QUESTIONS FOR DISCUSSION
8 OBJECTIVES AND OUTCOMES
8.1 INTRODUCTION
8.2 OBJECTIVES
8.3 OUTCOMES
8.4 SURROGATE OUTCOMES
8.5 SOME SPECIAL ENDPOINTS
8.6 SUMMARY
8.7 QUESTIONS FOR DISCUSSION
9 TRANSLATIONAL CLINICAL TRIALS
9.1 INTRODUCTION
9.2 INFORMATION FROM TRANSLATIONAL TRIALS
9.3 SUMMARY
9.4 QUESTIONS FOR DISCUSSION
10 DOSE-FINDING DESIGNS
10.1 INTRODUCTION
10.2 PRINCIPLES
10.3 FIBONACCI AND RELATED DOSE-RANGING
10.4 DESIGNS USED FOR DOSE-FINDING
10.5 MORE GENERAL DOSE-FINDING ISSUES
10.6 SUMMARY
10.7 QUESTIONS FOR DISCUSSION
11 SAMPLE SIZE AND POWER
11.1 INTRODUCTION
11.2 PRINCIPLES
11.3 EARLY DEVELOPMENTAL TRIALS
11.4 SAFETY AND ACTIVITY STUDIES
11.5 COMPARATIVE TRIALS
11.6 ES TRIALS
11.7 OTHER CONSIDERATIONS
11.8 SUMMARY
11.9 QUESTIONS FOR DISCUSSION
12 THE STUDY COHORT
12.1 INTRODUCTION
12.2 DEFINING THE STUDY COHORT
12.3 ANTICIPATING ACCRUAL
12.4 INCLUSIVENESS, REPRESENTATION, AND INTERACTIONS
12.5 SUMMARY
12.6 QUESTIONS FOR DISCUSSION
13 TREATMENT ALLOCATION
13.1 INTRODUCTION
13.2 RANDOMIZATION
13.3 CONSTRAINED RANDOMIZATION
13.4 ADAPTIVE ALLOCATION
13.5 OTHER ISSUES REGARDING RANDOMIZATION
13.6 UNEQUAL TREATMENT ALLOCATION
13.7 RANDOMIZATION BEFORE CONSENT
13.8 SUMMARY
13.9 QUESTIONS FOR DISCUSSION
14 TREATMENT EFFECTS MONITORING
14.1 INTRODUCTION
14.2 ADMINISTRATIVE ISSUES IN TRIAL MONITORING
14.3 ORGANIZATIONAL ISSUES RELATED TO DATA
14.4 STATISTICAL METHODS FOR MONITORING
14.5 SUMMARY
14.6 QUESTIONS FOR DISCUSSION
15 COUNTING SUBJECTS AND EVENTS
15.1 INTRODUCTION
15.2 NATURE OF SOME SPECIFIC DATA IMPERFECTIONS
15.3 TREATMENT NONADHERENCE
15.4 SUMMARY
15.5 QUESTIONS FOR DISCUSSION
16 ESTIMATING CLINICAL EFFECTS
16.1 INTRODUCTION
16.2 DOSE-FINDING AND PK TRIALS
16.3 SA STUDIES
16.4 COMPARATIVE EFFICACY TRIALS (PHASE III)
16.5 STRENGTH OF EVIDENCE THROUGH SUPPORT INTERVALS
16.6 SPECIAL METHODS OF ANALYSIS
16.7 EXPLORATORY OR HYPOTHESIS-GENERATING ANALYSES
16.8 SUMMARY
16.9 QUESTIONS FOR DISCUSSION
17 PROGNOSTIC FACTOR ANALYSES
17.1 INTRODUCTION
17.2 MODEL-BASED METHODS
17.3 ADJUSTED ANALYSES OF COMPARATIVE TRIALS
17.4 NON–MODEL-BASED METHODS FOR PFAS
17.5 SUMMARY
17.6 QUESTIONS FOR DISCUSSION
18 REPORTING AND AUTHORSHIP
18.1 INTRODUCTION
18.2 GENERAL ISSUES IN REPORTING
18.3 CLINICAL TRIAL REPORTS
18.4 AUTHORSHIP
18.5 ALTERNATIVE WAYS TO DISSEMINATE RESULTS
18.6 SUMMARY
18.7 QUESTIONS FOR DISCUSSION
19 FACTORIAL DESIGNS
19.1 INTRODUCTION
19.2 CHARACTERISTICS OF FACTORIAL DESIGNS
19.3 TREATMENT INTERACTIONS
19.4 EXAMPLES OF FACTORIAL DESIGNS
19.5 PARTIAL, FRACTIONAL, AND INCOMPLETE FACTORIALS
19.6 SUMMARY
19.7 QUESTIONS FOR DISCUSSION
20 CROSSOVER DESIGNS
20.1 INTRODUCTION
20.2 ADVANTAGES AND DISADVANTAGES
20.3 ANALYSIS
20.4 SUMMARY
20.5 QUESTIONS FOR DISCUSSION
21 META-ANALYSES
21.1 INTRODUCTION
21.2 A SKETCH OF META-ANALYSIS METHODS
21.3 OTHER ISSUES
21.4 SUMMARY
21.5 QUESTIONS FOR DISCUSSION
22 MISCONDUCT AND FRAUD IN CLINICAL RESEARCH
22.1 INTRODUCTION
22.2 RESEARCH PRACTICES
22.3 GENERAL APPROACH TO ALLEGATIONS OF MISCONDUCT
22.4 CHARACTERISTICS OF SOME MISCONDUCT CASES
22.5 LESSONS
22.6 CLINICAL INVESTIGATORS’ RESPONSIBILITIES
22.7 SUMMARY
22.8 QUESTIONS FOR DISCUSSION
Appendices
A DATA AND PROGRAMS
A.1 INTRODUCTION
A.2 DATA
A.3 DESIGN PROGRAMS
A.4 MATHEMATICA CODE
B NOTATION AND TERMINOLOGY
B.1 INTRODUCTION
B.2 NOTATION
B.3 TERMINOLOGY AND CONCEPTS
C ABBREVIATIONS
D NUREMBERG CODE
E DECLARATION OF HELSINKI
E.1 INTRODUCTION
E.2 BASIC PRINCIPLES FOR ALL MEDICAL RESEARCH
F NCI DATA AND SAFETY MONITORING POLICY
F.1 INTRODUCTION
F.2 RESPONSIBILITY FOR DATA AND SAFETY MONITORING
F.3 REQUIREMENT FOR DATA AND SAFETY MONITORING BOARDS
F.4 RESPONSIBILITIES OF THE DSMB
F.5 MEMBERSHIP
F.6 MEETINGS
F.7 RECOMMENDATIONS FROM THE DSMB
F.8 RELEASE OF OUTCOME DATA
F.9 CONFIDENTIALITY PROCEDURES
F.10 CONFLICT OF INTEREST
G NIH DATA AND SAFETY MONITORING POLICY
G.1 BACKGROUND
G.2 PRINCIPLES OF MONITORING DATA AND SAFETY
G.3 PRACTICAL AND IMPLEMENTATION ISSUES: OVERSIGHT OF MONITORING
G.4 INSTITUTES AND CENTERS RESPONSIBILITIES
G.5 PERFORMANCE OF DATA AND SAFETY MONITORING
G.6 EXAMPLES OF MONITORING OPERATIONS
H ROYAL STATISTICAL SOCIETY CODE OF CONDUCT
H.1 INTRODUCTION
H.2 CONSTITUTIONAL AUTHORITY
H.3 RULES OF PROFESSIONAL CONDUCT
BIBLIOGRAPHY
AUTHOR INDEX
SUBJECT INDEX
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Piantadosi, Steven.Clinical trials : a methodologic perspective / Steven Piantadosi—2nd ed. p. ; cm. – (Wiley series in probability and statistics) Includes bibliographical references and index. ISBN-13: 978-0-471-72781-1 (cloth : alk. paper) ISBN-10: 0-471-72781-4 (cloth : alk. paper) 1. Clinical trials—Statistical methods. I. Title. II. Series. [DNLM: 1. Biomedical Research—methods. 2. Research—methods. 3. Clinical Trials—methods. 4. Statistics—methods. W 20.5 P581c 2005]R853.C55P53 2005610′.72—dc222005040833
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PREFACE
A respectable period of time has passed since the first edition of this book, during which several pressures have necessitated a second edition. Most important, there were the inadvertent errors needing correction. The field has also had some significant changes, not so much in methodology perhaps as in context, regulation, and the like. I can say some things more clearly now than previously because many issues are better defined or I care more (or less) about how the discussion will be received.
I have added much new material covering some gaps (and therefore some new mistakes), but always hoping to make learning easier. The result is too much to cover in the period that I usually teach—one academic quarter. It may be appropriate for a semester course. Many students tell me that they consult this book as a reference, so the extra material should ultimately be useful.
Many colleagues have been kind enough to give their valuable time to review drafts of chapters, despite apparently violating Nabokov’s advice: “Only ambitious nonentities and hearty mediocrities exhibit their rough drafts. It is like passing around samples of one’s sputum.” Nevertheless, I am grateful to Elizabeth Garrett-Mayer, Ph.D., Bonnie Piantadosi, M.S.W, M.H.S., Anne Piantadosi, Irene Roach, Pamela Scott, Ph.D., Gail Weinmann, M.D., and Xiaobu Ye, M.D., M.S. for such help. Chris Szekely, Ph.D. reviewed many chapters and references in detail. Sean Roach, M.L.S. and Alisa Moore provided valuable assistance with references. Alla Guseynova, M.S. reviewed and corrected the computational code for the book.
As usual, students in my class Design and Analysis of Clinical Trials offered through the Hopkins Department of Biostatistics and the Graduate Training Program in Clinical Investigation provide the best motivation for writing this book by virtue of their lively discussion and questions. I would also like to thank the faculties, students, and sponsors from the AACR/ASCO Vail Workshop, as well as the sister workshops, FECS in Flims, Switzerland, and ACORD in Cairns, Australia, who have provided many practical questions and examples for interesting clinical trial designs over the years. Such teaching venues require conciseness and precision from a clinical trialist, and illustrate the very heart of collaborative research.
Finally there are two institutional venues that have helped motivate and shape my writing regarding clinical trials. One is the Protocol Review and Monitoring Committee in the Cancer Center, a scientific review forum on which I have served for many years. The second is the Institutional Review Board, on which I have more recently begun to serve. My colleagues in both of these settings encourage and take a careful, constructive, and detailed view of a multitude of diverse trials, and have taught me a great deal.
STEVEN PIANTADOSIBaltimore, Maryland, 2005
Books are fatal: they are the curse of the human race. Nine-tenths of existing books are nonsense, and the clever books are the refutation of that nonsense. The greatest misfortune that ever befell man was the invention of printing. [Benjamin Disraeli]
Writing is an adventure. To begin with, it is a toy and an amusement. Then it becomes a mistress, then it becomes a master, then it becomes a tyrant. The last phase is that just as you are about to be reconciled to your servitude, you kill the monster and fling him to the public. [Winston Churchill]
Writing is easy; all you do is sit staring at a blank sheet of paper until the drops of blood form on your forehead. [Gene Fowler]
PREFACE TO THE FIRST EDITION
In recent years a great deal has been written about clinical trials and closely related areas of biostatistics, biomathematics, biometry, epidemiology, and clinical epidemiology. The motive for writing this book is that there still seems to be a need among both physicians and biostatisticians for direct, relevant accounts of basic statistical methods in clinical trials. The need for both trialists and clinical investigators to learn about good methodology is particularly acute in oncology, where investigators search for treatment advances of great clinical importance, but modest size relative to the variability and bias which characterize studies of human disease. A similar need with the same motivation exists in many other diseases.
On the medical side of clinical trials, the last few years have seen a sharpened focus on training of clinical investigators in research methods. Training efforts have ranged from short intensive courses to research fellowships lasting years and culminating in a postgraduate degree. The evolution of teaching appears to be toward defining a specialty in clinical research. The material in this book should be of interest to those who take this path. The technical subjects may seem difficult at first, but the clinician should soon become comfortable with them.
On the biostatistical side of clinical trials, there has been a near explosion of methods in recent years. However, this is not a book on statistical theory. Readers with a good foundation in biostatistics should find the technical subjects practical and quite accessible. It is my hope that such students will see some cohesiveness to the field, fill in gaps in their knowledge, and be able to explore areas such as ethics and misconduct that are important to clinical trials.
There are some popular perceptions about clinical trials to which this book does not subscribe. For example, some widely used terminology regarding trials is unhelpful and I have attempted to counteract it by proposing alternatives. Also noncomparative trial designs (e.g., early developmental studies) are often inappropriately excluded from discussions of methods. I have tried to present concepts that unify all designed studies rather than ideas that artificially distinguish them. Dealing with pharmacokinetic-based designs tends to complicate some of the mathematics, but the concepts are essential for understanding these trials.
The book is intended to provide at least enough material for the core of a halfsemester course on clinical trials. In research settings where trials are used, the audience for such a course will likely have varying skills and directions. However, with a background of basic biostatistics, an introductory course in clinical trials or research methods, and appropriate didactic discussion, the material presented here should be useful to a heterogeneous group of students.
Many individuals have contributed to this book in indirect, but significant ways. I am reminded of two colleagues, now deceased, who helped to shape my thinking about clinical trials. David P. Byar, M.D. nurtured my early academic and quantitative interest in clinical trials in the early 1980s at the National Institutes of Health. After I joined the Johns Hopkins School of Medicine, Brigid G. Leventhal, M.D. showed me a mature, compassionate, and rigorous view of clinical trials from a practitioner’s perspective. I hope the thoughts in this book reflect some of the good attitudes and values of these fine scholars.
Other colleagues have taught me many lessons regarding clinical trials through their writings, lectures, conversations, and willingness to answer many questions. I would particularly like to thank Mitchell H. Gail, M.D., Ph.D. and Curtis L. Meinert, Ph.D. for much valuable advice and good example over the years. One of the most worthwhile experiences that a trial methodologist can have is to review and influence the designs of clinical trials while they are being developed. My colleagues at Johns Hopkins have cooperated in this regard through the Oncology Center’s Clinical Research Committee, especially Hayden Braine, M.D., who has chaired the Committee wisely for many years.
Through long-standing collaborations with the Lung Cancer Study Group, I met many clinical scholars with much to say and teach about trials. I would like to thank them, especially E. Carmack Holmes, M.D., John C. Ruckdeschel, M.D., and Robert Ginzberg, M.D. for showing me a model of interdisciplinary collaboration and friendship, which continued to outlive the financial arrangements. Recent collaborations with colleagues in the New Approaches to Brain Tumor Therapy Consortium have also enhanced my appreciation and understanding of early developmental trials.
In recent months many colleagues have assisted me by reading and offering comments on drafts of the chapters that follow. For this help, I would like to thank Lina Asmar, Ph.D., Tatiana Barkova, Ph.D., Jeanne DeJoseph, Ph.D., C.N.M., R.N., Suzanne Dibble, D.N.Sc., R.N., James Grizzle, Ph.D., Curt Meinert, Ph.D., Mitch Gail, M.D., Ph.D., Barbara Hawkins, Ph.D., Steven Goodman, M.D., Ph.D., Cheryl Enger, Ph.D., Guanghan Liu, Ph.D., J. Jack Lee, Ph.D., Claudia Moy, Ph.D., John O’Quigley, Ph.D., Thomas F. Pajak, Ph.D., Charles Rohde, Ph.D., Barbara Starklauf, M.A.S., Manel C. Wijesinha, Ph.D., and Marianna Zahurak, M.S. Many of the good points belong to them—the errors are mine.
Students in my classes on the Design and Analysis of Clinical Trials and Design of Experiments at the Johns Hopkins School of Hygiene and Public Health have contributed to this book by working with drafts, making helpful comments, working problems, or just discussing particular points. I would especially like to thank Maria Deloria, Kathleen Weeks, Ling-Yu Ruan, and Jeffrey Blume for their input. Helen Cromwell and Patty Hubbard have provided a great deal of technical assistance with the preparation of the manuscript. Gary D. Knott, Ph.D., Barry J. Bunow, Ph.D., and the staff at Civilized Software, Bethesda, Maryland (www.civilized.com) furnished me with MLAB software, without which many tasks in the following pages would be difficult.
This book was produced in using Scientific Workplace version 2.5. I am indebted to Kathy Watt of TCI Software Research in Las Cruces, New Mexico, (www.tcisoft.com) for assistance in preparing the style. A special thanks goes to Irene Roach for editing an early draft of the manuscript and to Sean Roach for collecting and translating hard-to-find references.
It is not possible to write a book without stealing a large amount of time from one’s family. Bonnie, Anne L., and Steven T. not only permitted this to happen but also were supportive, helpful, and understood why I felt it was necessary. I am most grateful to them for their patience and understanding throughout this project.
STEVEN PIANTADOSIBaltimore, Maryland,March 1997
1
PRELIMINARIES
1.1 INTRODUCTION
The best time to contemplate the quality of evidence from a clinical trial is before it begins. High-quality evidence about the effects of a new treatment is a consequence of good study design and execution, which themselves are the results of careful planning. This book attempts to acquaint investigators with ideas of design methodology that are helpful in planning, conducting, analyzing, and assessing clinical trials. The discussion covers a number of subjects relevant to early planning and design; some that find general agreement among methodologists and others that are contentious. It is unlikely that a reader experienced with clinical trials will agree with all that I say or what I choose to emphasize, but my perspective should be mainstream, internally consistent, and useful for learning.
This book is not intended to be an introduction to clinical trials. It should be part of a one- or two-quarter second structured postgraduate course for an audience with quantitative skills and a biological focus. The first edition evolved over a dozen years from the merging of two courses: one in experimental design and one in clinical trials. This second edition is the result of seven additional years of teaching and concomitant changes in the field. The book assumes a working knowledge of basic biostatistics and some familiarity with clinical trials, either didactic or practical. It is also helpful if the reader understands some more advanced statistical concepts, especially lifetables, survival models, and likelihoods. I recognize that clinicians often lack this knowledge. However, many contemporary medical researchers are seeking the required quantitative background through formal training in clinical investigation methods or experimental therapeutics. No clinical knowledge is needed to understand the concepts in this book, although it will be helpful throughout.
Many readers of this book will find the discussion uneven, ranging from basic to technically complex. This is partly a consequence of the very nature of clinical trials and partly the result of trying to address a heterogeneous population of students. My classes typically contain an equal mixture of biostatistics graduate students, medical doctors in specialty or subspecialty training (especially working toward a degree in clinical investigation), and other health professionals training to be sophisticated managers or consumers of clinical trials. For such an audience the goal is to provide breadth and to write so as not to be misunderstood.
This book should be supplemented with lecture and discussion, and possibly a computer lab. The reader who does not have an opportunity for formal classroom dialogue will need to explore the references more extensively. Exercises and discussion questions are provided at the end of each chapter. Most are intentionally made open- ended, with a suggestion that the student answer them in the form of a one- or two-page memorandum, as though providing an expert opinion to less-experienced investigators.
1.2 AUDIENCE AND SCOPE
The audience for this book is clinical trialists. It is not a simple matter to define a clinical trialist, but operationally it is someone who is immersed in the science of trials. Being a truly interdisciplinary field, trialists can be derived from a number of sources: (1) quantitative or biostatistical, (2) administrative or managerial, (3) clinical, or (4) ethical. Therefore students can approach the subject primarily from any of these perspectives.
It is common today for rigorous trialists to be strongly statistical. This is because of the fairly rapid recent pace of methods for clinical trials coming from that field, and also because statistics pertains to all of the disciplines in which trials are conducted. However, the discussion in this book does not neglect the other viewpoints that are also essential to understanding trials. Many examples will relate to cancer because that is the primary field in which I work, but the concepts will generalize to other areas.
Scientists who specialize in clinical trials are frequently dubbed “statisticians.” I will sometimes use that term with the following warning regarding rigor: statistics is an old and broad profession. There is not a one-to-one correspondence between statisticians or biostatisticians and knowledge of clinical trials. However, trial methodologists, whether statisticians or not, are likely to know a lot about biostatistics and will be accustomed to working with statistical experts. Many trial methodologists are not statisticians at all, but evolve from epidemiologists or clinicians with a strongly quantitative orientation, as indicated above.
I have made an effort to delineate and emphasize principles common to all types of trials: translational, developmental, safety, comparative, and large-scale studies. This follows from a belief that it is more helpful to learn about the similarities among trials rather than differences. However, it is unavoidable that distinctions must be made and the discussion tailored to specific types of studies. I have tried to keep such distinctions, which are often artificial, to a minimum. Various clinical contexts also treat trials differently, a topic discussed briefly in Chapter 4.
There are many important aspects of clinical trials not covered here in any detail. These include administration, funding, conduct, quality control, and the considerable infrastructure necessary to conduct trials. These topics might be described as the technology of trials, whereas my intent is to focus on the science of trials. Technology is vitally important, but falls outside of the scope of this book. Fortunately there are excellent sources for this material.
No book can be a substitute for regular interaction with a trial methodologist during both the planning stages of a clinical investigation and its analysis. I do not suggest passive reliance on such consultations, but intend to facilitate disseminating knowledge from which true collaborations between clinicians and trialists will result. Although many clinicians think of bringing their final data to a statistician, a collaboration will be most valuable during the design phase of a study when an experienced trialist may prevent serious methodologic errors, help streamline a study, or suggest ways to avoid costly mistakes.
The wide availability of computers is a strong benefit for clinical researchers, but presents some dangers. Although computers facilitate efficient, accurate, and timely keeping of data, modern software also permits or encourages researchers to produce “statistical” reports without much attention to study design and without fully understanding assumptions, methods, limitations, and pitfalls of the procedures being employed. Sometimes a person who knows how to run procedure-oriented packages on computerized data is called the “statistician,” even though he or she might be a novice at the basic theory underlying the analyses. It then becomes possible to produce a final report of a trial without the clinical investigator understanding the limitations of analysis and without the analyst being conversant with the data. What a weak chain this is.
The ideas in this book are intended to counteract these tendencies, not by being old-fashioned but by being rigorous. Good design inhibits errors by involving a statistical expert in the study as a collaborator from the beginning. Most aspects of the study will improve as a result, including reliability, resource utilization, quality assurance, precision, and the scope of inference. Good design can also simplify analyses by reducing bias and variability and removing the influence of complicating factors. In this way number crunching becomes less important than sound statistical reasoning.
The student of clinical trials should also understand that the field is growing and changing in response to both biological and statistical developments. A picture of good methodology today may be inadequate in the near future. This is probably more true of analytic methods than design, where the fundamentals will change more slowly. Analysis methods often will depend on new statistical developments or theory. These in turn depend on (1) computing hardware, (2) reliable and accessible software, (3) training and re-training of trialists in the use of new methods, (4) acceptance of the procedure by the statistical and biological communities, and (5) sufficient time for the innovations to diffuse into practice.
It is equally important to understand what changes or new concepts do not improve methodology but are put forward in response to non-science issues or because of creeping regulation. The best recent example of this is the increasing sacrifice of expertise in favor of objectivity in the structure and function of clinical trial monitoring (discussed in Chapter 14). Such practices are sometimes as ill considered as they are well meaning, and may be promulgated by sponsors without peer review or national consensus.
Good trial design requires a willingness to examine many alternatives within the confines of reliably answering the basic biological question. The most common errors related to trial design are devoting insufficient resources or time to the study, rigidly using standard types of designs when better (e.g., more efficient) designs are available, or undoing the benefits of a good design with a poorly planned (or executed) analysis. I hope that the reader of this book will come to understand where there is much flexibility in the design and analysis of trials and where there is not.
1.3 OTHER SOURCES OF KNOWLEDGE
The periodical literature related to clinical trials is large. I have attempted to provide current useful references for accessing it in this book. Aside from individual study reports in many clinical journals, there are some periodicals strongly related to trials. One is Controlled Clinical Trials, which has been the official journal of the Society for Clinical Trials (SCT) (mostly a U.S. organization). The journal was begun in 1980 and is devoted to trial methodology. The SCT was founded in 1981 and its 1500 members meet yearly. In 2003 the SCT changed its official journal to Clinical Trials, the first issue of which appeared in January 2004. This reincarnated journal should be an excellent resource for trialists. A second helpful periodical source is Statistics in Medicine, which frequently has articles of interest to the trialist. It began publication in 1982 and is the official publication of the International Society for Clinical Biostatistics (mostly a European organization). These two societies have begun joint meetings every few years.
Many papers of importance to clinical trials and related statistical methods appear in various other applied statistical and clinical journals. Reviews of many methods are published in Statistical Methods in Medical Research. One journal of particular interest to drug development researchers is the Journal of Biopharmaceutical Statistics. A useful general reference source is the journal Biostatistica, which contains abstracts from diverse periodicals. Statistical methodology for clinical trials appears in several journals. The topic was reviewed with an extensive bibliography by Simon (1991). A more extensive bibliography covering trials broadly has been given by Hawkins (1991).
In addition to journals there are a number of books and monographs dealing with clinical trials. The text by Meinert (1986) is a practical view of the infrastructure and administrative supports necessary to perform quality trials, especially randomized controlled trials. Freidman, Furberg, and DeMets (1982) and Pocock (1996) also discuss many conceptual and practical issues in their excellent books, which do not require extensive statistical background. A nice encyclopedic reference regarding statistical methods in clinical trials is provided by Redmond and Colton (2001). There is a relatively short and highly readable methodology book by Silverman (1985), and a second more issue oriented one (Silverman, 1998) with many examples. Every trialist should read the extensive work on placebos by Shapiro and Shapiro (1997).
In recent years many Web-based sources of information regarding clinical trials have been developed. The quality, content, and usefulness are highly variable and the user must consider the source when browsing. Resources of generally high quality that I personally find useful are listed in Table 1.1. My list is probably not complete with regard to specialized needs, but it provides a good starting point.
In the field of cancer trials, Buyse, Staquet, and Sylvester (1984) is an excellent source, although now becoming slightly dated. A contemporary view of cancer trials is given by Girling et al. (2003). The book by Leventhal and Wittes (1988) is useful for its discussion of issues from a strong clinical orientation. A very readable book with many good examples is that by Green, Benedetti, and Crowley (2002). In the field of AIDS, a useful source is Finkelstein and Schoenfeld (1995). The serious student should also be familiar with the classic papers by Peto et al. (1977a, b).
Spilker (e.g., 1993) has written a large volume of material about clinical trials, much of it oriented toward pharmaceutical research and not statistical methods. Another useful reference with an industry perspective is Wooding (1994). Data management is an important subject for investigators, but falls outside the scope of this book. The subject is probably made more complex by the fact that vastly more data are routinely collected during developmental trials than are needed to meet the objectives. Good sources of knowledge concerning data management include the books by McFadden (1998) and that edited by Rondel, Varley, and Webb (1993), and an issue of Controlled Clinical Trials (April 1995) devoted to data management. Books on other relevant topics will be mentioned in context later.
TABLE 1.1 Some Web Resources for Clinical Trials Information
Link
Description
assert-statement.org
A standard for the scientific and ethical review of trials: a structured approach for ethics committees reviewing randomized controlled clinical trials.
cochrane.org
The Cochrane Collaboration provides up-to-date information about the effects of health care.
clinicaltrials.gov
Provides information about federally and privately supported clinical research.
consort-statement.org
CONSORT statement: an evidence-based tool to improve the quality of reports of randomized trials.
jameslindlibrary.org
Evolution of fair tests of medical treatments; examples from books and journal articles including key passages of text.
icmje.org
Uniform requirements for manuscripts submitted to biomedical journals
gpp-guidelines.org
Encourages responsible and ethical publication of clinical trials sponsored by pharmaceutical companies
ncbi.nlm.nih.gov/entrez/query.fcgi
PubMed: includes over 14 million citations for biomedical articles back to the 1950’s
mcclurenet.com/ICHefficacy.html
ICH efficacy guidelines
controlled-trials.com
Current controlled trials: provides access to peer reviewed biomedical research
Even in a very active program of clinical research, a relatively short exposure to the practical side of clinical trials cannot illustrate all the important lessons. This is because it may take years for any single clinical trial, and many such studies, to yield all of their information useful for learning about methodology. Even so, the student of clinical trials will learn some lessons more quickly by being involved in an actual study, compared with simply studying theory. In this book, I illustrate many concepts with published trials. In this way the reader can have the benefit of observing studies from a long-term perspective, which would otherwise be difficult to acquire.
1.3.1 Terminology
The terminology of clinical trials is not without its ambiguities. A recent firm effort has been made to standardize definitions in a dictionary devoted to clinical trial terminology (Meinert, 1996). Most of the terms within this book are used in a way consistent with such definitions. A notable exception is that I propose and employ explanatory alternatives to the widely used, uninformative, inconsistent, and difficult-to-generalize “phase I, II, III, or IV” designations for clinical trials. This topic is discussed in Chapter 6.
Much of the terminology of clinical trials has been derived directly from drug development. Because of the heavy use of clinical trials in the development of cytotoxic drugs for the treatment of cancer between the 1960s and the 1990s, the terminology for this setting has found its way inappropriately into other contexts. Drug development terminology is often ambiguous and inappropriate for clinical trials performed in many other areas, and is even unsuitable for many new cancer therapies that do not act through a direct cytotoxic mechanism. For this reason I have not employed this outdated terminology in this book and have used descriptive alternatives (Section 6.3.2).
1.3.2 Review of Notation and Terminology Is Helpful
There is no escaping the need for mathematical formalism in the study of clinical trials. It would be unreasonable, a priori, to expect mathematics to be as useful as it is in describing nature (Wigner, 1959). Nevertheless, it is, and the mathematics of probability is the particular area most helpful for clinical trials. Galileo said:
The book of the universe is written in mathematical language, without which one wanders in vain through a dark labyrinth.
Statisticians light their dark labyrinth using abstract symbols (e.g., Greek letters) as a shorthand for important mathematical quantities and concepts. I will also use these symbols when appropriate in this book, because many ideas are troublesome to explain without good notation.
However, because this book is not oriented primarily toward statistical theory, the use of symbols will be minimal and tolerable, even to nonmathematical readers. A review and explanation of common usage of symbols consistent with the clinical trials literature is given in Appendix B. Because some statistical terms may be unfamiliar to some readers, definitions and examples are also listed in that chapter. Abbreviations used in the book are also explained there.
This book does not and cannot provide the technical statistical background that is needed to understand clinical trial design and analysis thoroughly. As stated above, much of this knowledge is assumed to be present. Help is available in the form of practical and readable references. Examples are the books by Armitage and Berry (1994) and Marubini and Valsecchi (1995). More concise summaries are given by Campbell and Machin (1990) and Everitt (1989). A comprehensive reference with good entries to the literature is the Encyclopedia of Biostatistics (Armitage and Colton, 1998). More specialized references will be mentioned later.
1.4 EXAMPLES, DATA, AND PROGRAMS
It is not possible to learn all the important lessons about clinical trials from classroom instruction or reading, nor is it possible for every student to be involved with actual trials as part of a structured course. This problem is most correctable for topics related to the analysis of trial results, where real data can be provided. For some examples and problems used in this book, the data are provided through the author’s Web site (www.cancerbiostats.onc.jhmi.edu/). Throughout the book, I have made a concerted effort to provide examples of trials that are instructive but small, so as to be digestible by the student. Computerized data files and programs to read and analyze them are provided on the Web site. It also contains some sample size and related programs that are helpful for design calculations. More powerful sample size (and other) design software that is available commercially is discussed in Chapter 11.
Many, but not all, tables, figures, and equations in the text have been programmed in Mathematica, Version 5 (Wolfram, 2003). The computer code related to the book is available from the author’s Web site. Mathematica, which is required to use the relevant programs, is commercially available. The stand-alone programs mentioned above and Mathematica code are made available for instructional purposes without warranty of any kind—the user assumes responsibility for all results.
1.5 SUMMARY
The premise of this book is that well-designed experimental research is a necessary basis for therapeutic development and clinical care decisions. The purpose of this book is to address issues in the methodology of clinical trials in a format accessible to interested statistical and clinical scientists. The audience is intended to be practicing clinicians, statisticians, trialists, and others with a need for understanding good clinical research methodology. The reader familiar with clinical trials will notice a few substantive differences from usual discussions, including the use of descriptive terms for types of trials and an even-handed treatment of different statistical perspectives. Examples from the clinical trials literature are used, and data and computer programs for some topics are available. A review of essential notation and terminology is also provided.
2
CLINICAL TRIALS AS RESEARCH
2.1 INTRODUCTION
In the late nineteenth and early twentieth century, therapeutics was in a state of nihilism. Nineteenth-century science had discovered that many diseases improved without therapy, and that many popular treatments, among them certain natural products and bloodletting, were ineffective. The nihilism was likely justifiable because it could be claimed that the entire history of therapeutics up to that point was essentially only the history of the placebo effect (Shapiro and Shapiro, 1997). However, when scientists showed that diseases like pellagra and diabetes could have their effects relieved with medicinals, belief in treatment began to return. Following the discovery of penicillin and sulfanilamide in the twentieth century, the period of nihilism ended (Thomas, 1977; Coleman, 1987).
More recently discovery of effective drugs for the treatment of cancer, cardiovascular disease, infections, and mental illness as well as the crafting of vaccines and other preventive measures have demonstrated the value of therapeutics. There is economic evidence and opinion to support the idea that the strong overall economic status of the United States is substantially due to improved health of the population (Funding First, 2000), itself dependent on effective public health and therapeutic interventions. Clinical investigation methods are important in the search for effective prevention agents and treatments, sorting out the benefits of competing therapies, and establishing optimum treatment combinations and schedules.
Experimental design and analysis have become essential because of the greater detail in modern biological theories and the complexities in treatments of disease. The clinician is usually interested in small, but biologically important, treatment effects that can be obscured by uncontrolled natural variation and bias in nonrigorous studies. This places well-performed clinical trials at the very center of clinical research today, although the interest in small effect sizes also creates problems for other aspects of clinical investigation (Ahrens, 1992).
Other contemporary pressures also encourage the application of rigorous clinical trials. Societal expectations to relieve suffering through medical progress, governmental regulation of prescription drugs and devices, and the economics of pharmaceutical development all encourage or demand efficient and valid study design. Nearly all good clinical trials have basic biological, public health, and commercial value, encouraging investigators to design studies that yield timely and reliable results.
In the early twenty-first century the pendulum of nihilism has swung strongly in the opposite direction. Today there is belief in the therapeutic efficacy of many treatments. Traditional medicine, complimentary, alternative, fringe, and other methods abound, with their own advocates and practitioners. Many patients put their confidence in untested or inadequately tested treatments. Even in disease areas where therapies are evaluated rigorously, many patients assume treatments are effective, or at least worth the risk, or they would not be under investigation. Other patients are simply willing to take a chance that a new treatment will work, especially when the side effects appear to be minimal.
To a rigorous modern clinical investigator, these comprise opportunities to serve the needs of patients and practitioners by providing the most reliable evidence about treatment effects and risks. These circumstances often provide pressure to use clinical trials. However, the same forces can create incentives to bypass rigorous evaluation methods because strong beliefs of efficacy can arise from unreliable data, as has been the case historically. Whether contemporary circumstances encourage or discourage clinical trials depends largely on mindset and values.
A trialist must understand two different modes of thinking that support the science—clinical and statistical. They both underlie the re-emergence of therapeutics as a modern science. Each method of reasoning arose independently and must be combined skillfully if they are to serve therapeutic questions effectively.
2.1.1 Clinical Reasoning Is Based on the Case History
The word clinical is derived from the Greek kline, which means bed. In modern usage, clinical not only refers to the bedside but pertains more generally to the care of human patients. The quantum unit of clinical reasoning is the case history, and the primary focus of clinical inference is the individual patient. Before the widespread use of experimental trials, clinical methods of generalizing from the individual to the population were informal. The concepts of person-to-person variability and its sources were also described informally. Medical experience and judgment was not, and probably cannot be, captured in a set of rules. Instead, it is a form of “tacit knowledge” (Polanyi, 1958), and is very concrete.
New and potentially useful clinical observations are made against this background of reliable experience. Following such observation, many advances have been made by incremental improvement of existing ideas. This process explains much of the progress made in medicine and biology up to the twentieth century. Incremental improvement is a reliable but slow method that can optimize many complex processes. For example, the writing of this book proceeded largely by slightly improving earlier drafts, especially true for the second edition. However, there was a foundation of design that greatly facilitated the entire process. Clinical trials can provide a similar foundation of design for clinical inference, greatly amplifying the benefits of careful observation.
There often remains discomfort in clinical settings over the extent to which population-based estimates (i.e., those from a clinical trial) pertain to any individual, especially a new patient outside the study. This is not so much a problem interpreting the results of a clinical trial as a difficulty trying to use results to select the best treatment for a new individual. There is no formal way to accomplish this generalization in a purely clinical framework. It depends on judgment, which itself depends on experience. However, clinical experience historically has been summarized in nonstatistical ways.
A stylized example of clinical reasoning, and a rich microcosm of issues, can be seen in the following case history, transmitted by Francis Galton (1899):
The season of strawberries is at hand, but doctors are full of fads, and for the most part forbid them to the gouty. Let me put heart to those unfortunate persons to withstand a cruel medical tyranny by quoting the experience of the great Linnæus. It will be found in the biographical notes, written by himself in excellent dog-latin, and published in the life of him by Dr. H. Stoever, translated from German into English by Joseph Trapp, 1794. Linnæus describes the goutiness of his constitution in p. 416 (cf. p. 415) and says that in 1750 he was attacked so severely by siatica that he could hardly make his way home. The pain kept him awake during a whole week. He asked for opium, but a friend dissuaded it. Then his wife suggested “Won’t you eat strawberries?” It was the season for them. Linnæus, in the spirit of experimental philosopher, replied, “tentabo—I will make the trial.” He did so, and quickly fell into a sweet sleep that lasted two hours, and when he awoke the pain had sensibly diminished. He asked whether any strawberries were left: there were some, and he ate them all. Then he slept right away till morning. On the next day, he devoured as many strawberries as he could, and on the subsequent morning the pain was wholly gone, and he was able to leave his bed. Gouty pains returned at the same date in the next year, but were again wholly driven off by the delicious fruit; similarly in the third year. Linnæus died soon after, so the experiment ceased.
What lucrative schemes are suggested by this narrative. Why should gouty persons drink nasty waters, at stuffy foreign spas, when strawberry gardens abound in England? Let enthusiastic young doctors throw heart and soul into the new system. Let a company be run to build a curhaus in Kent, and let them offer me board and lodging gratis in return for my valuable hints.
The pedigree of the story may have been more influential than the evidence it provides. It has been viewed both as quackery and as legitimate (Porter and Rousseau, 1998), but as a trialist and occasional sufferer of gout, I find the story both quaint and enlightening with regard to a clinical mindset. Note especially how the terms “trial” and “experiment” were used, and the tone of determinism.
Despite its successes clinical reasoning by itself has no way to deal formally with a fundamental problem regarding treatment inefficacy. Simply stated, that problem is “why do ineffective treatments frequently appear to be effective?” The answer to this question may include some of the following reasons:
The disease has finished its natural course
There is a natural exacerbation–remission cycle
Spontaneous cure has occurred
The placebo effect
There is a psychosomatic cause and, hence, a cure by suggestion
The diagnosis is incorrect
Relief of symptoms has been confused with cure
Distortions of fact by the practitioner or patient
Chance
However, the attribution of effect to one or more of these causes cannot be made reliably using clinical reasoning alone.
Of course, the same types of factors can make an effective treatment appear ineffective, a circumstance for which pure clinical reasoning also cannot offer reliable remedies. This is especially a problem when seeking small magnitude, but clinically important, benefits. The solution offered by statistical reasoning is to control the signal-to-noise ratio by design.
2.1.2 Statistical Reasoning Emphasizes Inference Based on Designed Data Production
The word statistics is derived from the Greek statis and statista, which mean state. The exact origin of the modern usage of the term statistics is obscured by the fact that the word was used mostly in a political context to describe territory, populations, trade, industry, and related characteristics of countries from the 1500s until about 1850. A brief review of this history was given by Kendall, who stated that scholars began using data in a reasoned way around 1660 (Kendall, 1960). The word statistik was used in 1748 to describe a particular body of analytic knowledge by the German scholar Gottfried Achenwall (1719–1772) in Vorbereitung zur Staatswissenschaft (Achenwall, 1748; Hankins, 1930). The context seems to indicate that the word was already used in the way we mean it now, but some later writers suggest that he originated it (Fang, 1972; Liliencron, 1967). Porter (1986) gives the date for the use of the German term statistik as 1749.
Statistics is a highly developed information science. It encompasses the formal study of the inferential process, especially the planning and analysis of experiments, surveys, or observational studies. It became a distinct field of study only in the twentieth century (Stigler, 1986). Although based largely on probability theory, statistics is not, strictly speaking, a branch of mathematics. Even though the same methods of axioms, formal deductive reasoning, and logical proof are used in both statistics and mathematics, the fields are distinct in origin, theory, practice, and application. Barnett (1982) discusses various views of statistics, eventually defining it as:
the study of how information should be employed to reflect on, and give guidance for action in, a practical situation involving uncertainty.
Making reasonable, accurate, and reliable inferences from data in the presence of uncertainty is an important and far-reaching intellectual skill. It is not merely a collection of ad hoc tricks and techniques, an unfortunate view occasionally held by clinicians and some grant reviewers. Statistics is a way of thinking or an approach to everyday problems that relies heavily on designed data production. An essential impact of statistical thought is that it minimizes the chance of drawing incorrect conclusions from either good or bad data.
Modern statistical theory is the product of extensive intellectual development in the early to middle twentieth century and has found application in most areas of science. It is not obvious in advance that such a theory should be applicable across a large number of disciplines. That is one of the most remarkable aspects of statistical theory. Despite the wide applicability of statistical reasoning, it remains an area of substantial ignorance for many scientists.
Statistical reasoning is characterized by the following general methods, in roughly this order:
Reasoning using these tools enhances validity and permits efficient use of information, time, and resources.
Perhaps because it embodies a sufficient degree of abstraction but remains grounded by practical questions, statistics has been very broadly successful. Through its mathematical connections, statistical reasoning permits or encourages abstraction that is useful for solving the problem at hand and other similar ones. This universality is a great advantage of abstraction. In addition abstraction can often clarify outcomes, measurements, or analyses that might otherwise be poorly defined. Finally abstraction is a vehicle for creativity.
2.1.3 Clinical and Statistical Reasoning Converge in Research
Because of their different origins and purposes, clinical and statistical reasoning could be viewed as fundamentally incompatible. But the force that combines these different types of reasoning is research. A clinical researcher is someone who investigates formal hypotheses arising from work in the clinic (Frei, 1982; Frei and Freireich, 1993). This requires two interdependent tasks that statistics does well: generalizing observations from few to many, and combining empirical and theory-based knowledge.
In the science of clinical research, empirical knowledge comes from experience, observation, and data. Theory-based knowledge arises from either established biology or hypothesis. In statistics, the empirical knowledge comes from data or observations, while the theory-based knowledge is that of probability and determinism, formalized in mathematical models. Models specifically, and statistics in general, are the most efficient and useful way to combine theory and observation.
This mixture of reasoning explains both the successful application of statistics broadly and the difficulty that some clinicians have in understanding and applying statistical modes of thought. In most purely clinical tasks, as indicated above, there is relatively little need for statistical modes of reasoning. The best use and interpretation of diagnostic tests is one interesting exception. Clinical research, in contrast, demands critical and quantitative views of research designs and data. The mixture of modes of reasoning provides a solution to the inefficacy problem outlined in Section 2.1.1. To perform, report, and interpret research studies reliably, clinical modes of reasoning must be reformed by statistical ideas. Carter, Scheaffer, and Marks (1986) focused on this point appropriately when they said:
Statistics is unique among academic disciplines in that statistical thought is needed at every stage of virtually all research investigations, including planning the study, selecting the sample, managing the data, and interpreting the results.
Failure to master statistical concepts can lead to numerous and important errors and biases in medical research, a compendium of which is given by Andersen (1990). Coincident with this need for statistical knowledge in the clinic, it is necessary for the clinical trials statistician to master fundamental biological and clinical concepts relevant to the disease under study. Failure to accomplish this can also lead to serious methodological and inferential errors. A clinical researcher must consult the statistical expert early enough in the conceptual development of the experiment to improve the study. The clinical researcher who involves a statistician only in the “analysis” of data from a trial can expect a substantially inferior product overall.
2.2 DEFINING CLINICAL TRIALS FORMALLY
2.2.1 Mixing of Clinical and Statistical Reasoning Is Recent
The historical development of clinical trials has depended mostly on biological and medical advances, as opposed to applied mathematical or statistical developments. A broad survey of mathematical advances in the biological and medical sciences supports this interpretation (Lancaster, 1994). For example, the experimental method was known to the Greeks, especially Strato of Lampsacus (c. 250 BCE) (Magner, 2002). The Greek anatomists, Herophilus and Erasistratis in the third century BCE, demonstrated by vivisection of prisoners that loss of movement or sensation occurred when nerves were severed. Such studies were not perpetuated, but it would be two millennia before adequate explanations for the observations would be formulated (F.R. Wilson, 1998; Staden, 1992).
There was considerable opposition to the application of statistics in medicine, especially in the late eighteenth century and early nineteenth century when methods were first being developed. The numerical method, as it was called, was proposed and developed in the early nineteenth century and has become most frequently associated with Pierre Charles Alexander Louis. His best-known and most controversial study was published in 1828, and examined the effects of bloodletting as treatment for pneumonia (see Louis, 1835). Although the results did not clearly favor or disfavor bloodletting, his work became controversial because it appeared to challenge conventional practice on the basis of numerical results. The study was criticized, in part, because the individual cases were heterogeneous and the number of patients was relatively small. There was even a claim in 1836 by d’Amador that the use of probability in therapeutics was antiscientific (d’Amador, 1836).
Opposition to the application of mathematical methods in biology also came from Claude Bernard (1865). His argument was also based partly on individual heterogeneity. Averages, he felt, were as obscuring as they were illuminating. Gavarret gave a formal specification of the principles of medical statistics in 1840 (Gavarret, 1840). He proposed that at least two hundred cases, and possibly up to five hundred cases were necessary for reliable conclusions. In the 1920s R.A. Fisher demonstrated and advocated the use of true experimental designs, especially randomization, in studying biological problems (Fisher, 1935; Box, 1980).
Yet it was the mid-1900s before the methodology of clinical trials began to be applied earnestly. The delay in applying existing quantitative methods to clinical problem solving was probably a consequence of many factors, including inaccurate models of disease, lack of development of drugs and other therapeutic options, physician resistance, an authoritarian medical system that relied heavily on expert opinion, and the absence of the infrastructure needed to support clinical trials. In fact the introduction of numerical comparisons and statistical methods into assessments of therapeutic efficacy has been resisted by medical practitioners at nearly every opportunity over the last 200 years (Matthews, 1995).
Even today in biomedical research institutions and clinical trial collaborations, there is a firm tendency toward the marginalization of statistical thinking. The contrast with the recognized and overwhelming utility of mathematics in the physical sciences is striking. Among others, Eugene Wigner (1959) pointed this out, stating that:
… the enormous usefulness of mathematics in the natural sciences is something bordering on the mysterious and that there is no rational explanation for it.
It is noteworthy that he referred broadly to natural science.
Part of the difficulty fitting statistics into clinical and biomedical science lies with the training of health professionals, particularly physicians who are often the ones responsible for managing clinical trials. Most medical school applicants have minimal mathematical skills, and the coverage of statistical concepts in medical school curricula is usually brief, if at all. Statistical reasoning is often not presented correctly or effectively in postgraduate training programs. The result is ignorance, discomfort, and the tendency to treat statistics as a post hoc service function. For example, consider the important synergistic role of biostatistics and clinical trials in cancer therapeutics over the last 40 years. Biostatistical resources in cancer centers sponsored by the National Institutes of Health are evaluated for funding using essentially the same guidelines and procedures as for resources such as glass washing, animal care, and electron microscopes (NCI, 2003).
An excellent historical review of statistical developments behind clinical trials is given by Gehan and Lemak (1994). Broader discussions of the history of trials are given by Bull (1959), Meinert (1986), and Pocock (1996). It is interesting to read early discussions of trial methods (e.g., Herdan, 1955) to see issues of concern that persist today.
Since the 1940s clinical trials have seen a widening scope of applicability. This increase is a consequence of many factors, including the questioning of medical dogma, notable success in applying experimental designs in both the clinical and basic science fields, governmental funding priorities, regulatory oversight of drugs and medical devices with its more stringent demands, and development of applied statistical methods. In addition the public, governmental, industrial, and academic response to important diseases like cardiovascular disease, cancer, and AIDS has increased the willingness of, and necessity for, clinicians to engage in structured experiments to answer important questions reliably (Gehan and Schneiderman, 1990; Greenhouse, 1990; Halperin, DeMets, and Ware, 1990). Finally the correct perception that well-done clinical trials are robust (i.e., insensitive to deviations from many underlying assumptions) has led to their wider use. Health professionals who wish to conduct and evaluate clinical research need to become competent in this field.
Discomfort with, and arguments against, the use of rigorous experimental methods in clinical medicine persist. No single issue is more of a focal point for such objections than the use of randomization because of the central role it plays in comparative trials. Typical complaints about randomization are illustrated by Abel and Koch (1997, 1999), who explicitly reject randomization as a (1) means to validate certain statistical tests, (2) basis for (causal) inference, (3) facilitation of masking, and (4) method to balance comparison groups. Similar arguments are given by Urbach (1993). These criticisms of randomization are extreme and I believe them to be incorrect. I will return to this discussion in Chapter 13.
2.2.2 Clinical Trials Are Rigorously Defined
An experiment is a series of observations made under conditions controlled by the scientist. To define an investigation as an experiment, the scientist must control the application of treatment (or intervention). In other words, the essential characteristic that distinguishes experimental from nonexperimental studies is whether or not the scientist controls or manipulates the treatment (factors) under investigation. In nonexperimental studies, treatments are applied to the subjects for reasons beyond the investigator’s control. The reasons why treatments were applied may be unknown, or possibly even known to be confounded with prognosis.
Frequently in experiments there is a second locus of control over extraneous influences. The play of chance is an influence that the scientist usually intends to reduce, for example. This additional control isolates the effect of treatment on outcome and makes the experiment efficient. However, efficiency is a relative concept and is not a required characteristic of a true experiment. Design is the process or structure that controls treatment administration and isolates the factors of interest.
A clinical trial is an experiment testing a medical treatment on human subjects. Based on the reasoning above, a control group internal to the study is not required to satisfy the definition. In particular, nonrandomized studies can be clinical trials. The clinical trialist also attempts to control extraneous factors that may affect inference about the treatment. Which factors to control depends on context, resources, and the type of inference planned. Generically, the investigator will control (minimize) factors that contribute to outcome variability, selection bias, inconsistent application of the treatment, and incomplete or biased ascertainment of outcomes. We might think of these extraneous factors as noise with random and nonrandom components.
Use of the term “observational” to describe medical studies that are not clinical trials (e.g., epidemiologic studies) is common but inaccurate. All scientific studies depend on observation. The best terminology to distinguish clinical trials from other studies is experimental versus nonexperimental. Conceptual plans for observation, data capture, follow-up of study participants, and analysis are similar for many types of medical studies.
