Lemuel A. Moyé – författare

Bayesian analyses have made important inroads in modern clinical research due, in part, to the incorporation of the traditional tools of noninformative priors as well as the modern innovations of adaptive randomization and predictive power. Presenting an introductory perspective to modern Bayesian procedures, Elementary Bayesian Biostatistics explores Bayesian principles and illustrates their application to healthcare research.Building on the basics of classic biostatistics and algebra, this easy-to-read book provides a clear overview of the subject. It focuses on the history and mathematical foundation of Bayesian procedures, before discussing their implementation in healthcare research from first principles. The author also elaborates on the current controversies between Bayesian and frequentist biostatisticians. The book concludes with recommendations for Bayesians to improve their standing in the clinical trials community. Calculus derivations are relegated to the appendices so as not to overly complicate the main text.As Bayesian methods gain more acceptance in healthcare, it is necessary for clinical scientists to understand Bayesian principles. Applying Bayesian analyses to modern healthcare research issues, this lucid introduction helps readers make the correct choices in the development of clinical research programs.

One of the most challenging issues for clinical trial investigators, sponsors, and regulatory officials is the interpretation of experimental results that are composed of the results of multiple statistical analyses. These analyses may include the effect of therapy on multiple endpoints, the assessment of a subgroup analysis, and the evaluation of a dose-response relationship in complex mixtures. Multiple Analyses in Clinical Trials: Fundamentals for Clinical Investigators is an essentially nonmathematical discussion of the problems posed by the execution of multiple analyses in clinical trials. It concentrates on the rationale for the analyses, the difficulties posed by their interpretation, easily understood solutions, and useful problem sets. This text will help clinical investigators understand multiple analysis procedures and the key issues when designing their own work or reviewing the research of others. This book is written for advanced medical students, clinical investigators at all levels, research groups within the pharmaceutical industry, regulators at the local, state, and federal level, and biostatisticians.Only a basic background in health care and introductory statistics is required. Dr. Lemuel A. Moye, M.D., Ph.D. is a physician and Professor of Biometry at the University of Texas School of Public Health. He has been Co-Principal Investigator of two multinational clinical trials examining the role of innovative therapy in post myocardial infarction survival (SAVE) and the use of cholesterol reducing agents in post myocardial infarction survival in patients with normal cholesterol levels (CARE). He has authored over one hundred articles in journals such as the Journal of the American Medical Association, the New England Journal of Medicine, Statistics in Medicine, and Controlled Clinical Trials.

Statistical Monitoring of Clinical Trials: Fundamentals for Investigators introduces the investigator and statistician to monitoring procedures in clinical research. Clearly presenting the necessary background with limited use of mathematics, this book increases the knowledge, experience, and intuition of investigations in the use of these important procedures now required by the many clinical research efforts. The author provides motivated clinical investigators the background, correct use, and interpretation of these monitoring procedures at an elementary statistical level. He defines terms commonly used such as group sequential procedures and stochastic curtailment in non-mathematical language and discusses the commonly used procedures of Pocock, O’Brien–Fleming, and Lan–DeMets. He discusses the notions of conditional power, monitoring for safety and futility, and monitoring multiple endpoints in the study. The use of monitoring clinical trials is introduced in the context of the evolution of clinical research and one chapter is devoted to the more recent Bayesian procedures. From the reviews:"The author has a wealth of experience in this area and this is demonstrated throughout the text with relevant poignant examples." Short Book Reviews of the ISI, June 2006

Lowers the Learning Curve for Physicians and Researchers!The successful Statistical Reasoning in Medicine: The Intuitive P-value Primer, with its novel emphasis on patient and community protection, illustrated the correct use of statistics in health care research for healthcare workers. Through clear explanations and examples, this book provided the non-mathematician with a foundation for understanding the underlying statistical reasoning process in clinical research, the core principles of research design, and the correct use of statistical inference and p-values.The P-Value Primer 2nd Edition levels the learning curve of statistics for health care researchers by further de-emphasizing mathematical and computational devices, bringing the principles of statistical reasoning closer to the uninitiated. Adding to the updated discussions of research design, hypothesis testing, regression analysis, and Bayes procedures, are new discussions of absolute and relative risk, as well as a lucid description of the number needed to treat (NNT). The multiple analysis issue is clearly defined, and a new description of the correct use and interpretation of combined endpoints in health care research is offered in an easily digestible format.The P-value Primer 2nd Edition demolishes other obstacles that have impeded a clear understanding of the application of statistics in medicine. The intertwined roles of epidemiology and biostatistics are depicted. In addition to a description of the non-technical history of statistics, a new discussion describes the active cultural forces that have historically argued against the use of probability and statistics, placing the current applications and controversies involving p-values in context. New illustrations of the difficulties physicians and health care providers face in research are offered, and the differences between research skills and statistical skills are distinguished. New discussion describing the process of scientific reasoning, p-values, and the law is included. All of this nonstandard content, so essential for a well rounded perspective on the modern use of statistics in medicine, makes this volume unique among introductory statistics books.New figures, conversation, and illustrations fortify each chapter. In addition, three new appendices have been added on the normal distribution, sample size computations, and new requirements for the use of statistics in the courtroom.

Mathematical statistics typically represents one of the most difficult challenges in statistics, particularly for those with more applied, rather than mathematical, interests and backgrounds. Most textbooks on the subject provide little or no review of the advanced calculus topics upon which much of mathematical statistics relies and furthermore contain material that is wholly theoretical, thus presenting even greater challenges to those interested in applying advanced statistics to a specific area.Mathematical Statistics with Applications presents the background concepts and builds the technical sophistication needed to move on to more advanced studies in multivariate analysis, decision theory, stochastic processes, or computational statistics. Applications embedded within theoretical discussions clearly demonstrate the utility of the theory in a useful and relevant field of application and allow readers to avoid sudden exposure to purely theoretical materials.With its clear explanations and more than usual emphasis on applications and computation, this text reaches out to the many students and professionals more interested in the practical use of statistics to enrich their work in areas such as communications, computer science, economics, astronomy, and public health.

Mathematical statistics typically represents one of the most difficult challenges in statistics, particularly for those with more applied, rather than mathematical, interests and backgrounds. Most textbooks on the subject provide little or no review of the advanced calculus topics upon which much of mathematical statistics relies and furthermore contain material that is wholly theoretical, thus presenting even greater challenges to those interested in applying advanced statistics to a specific area.Mathematical Statistics with Applications presents the background concepts and builds the technical sophistication needed to move on to more advanced studies in multivariate analysis, decision theory, stochastic processes, or computational statistics. Applications embedded within theoretical discussions clearly demonstrate the utility of the theory in a useful and relevant field of application and allow readers to avoid sudden exposure to purely theoretical materials.With its clear explanations and more than usual emphasis on applications and computation, this text reaches out to the many students and professionals more interested in the practical use of statistics to enrich their work in areas such as communications, computer science, economics, astronomy, and public health.

One of the most challenging issues for clinical trial investigators, sponsors, and regulatory officials is the interpretation of experimental results that are composed of the results of multiple statistical analyses. These analyses may include the effect of therapy on multiple endpoints, the assessment of a subgroup analysis, and the evaluation of a dose-response relationship in complex mixtures. Multiple Analyses in Clinical Trials: Fundamentals for Clinical Investigators is an essentially nonmathematical discussion of the problems posed by the execution of multiple analyses in clinical trials. It concentrates on the rationale for the analyses, the difficulties posed by their interpretation, easily understood solutions, and useful problem sets. This text will help clinical investigators understand multiple analysis procedures and the key issues when designing their own work or reviewing the research of others. This book is written for advanced medical students, clinical investigators at all levels, research groups within the pharmaceutical industry, regulators at the local, state, and federal level, and biostatisticians. Only a basic background in health care and introductory statistics is required. Dr. Lemuel A. Moyé, M.D., Ph.D. is a physician and Professor of Biometry at the University of Texas School of Public Health. He has been Co-Principal Investigator of two multinational clinical trials examining the role of innovative therapy in post myocardial infarction survival (SAVE) and the use of cholesterol reducing agents in post myocardial infarction survival in patients with normal cholesterol levels (CARE). He has authored over one hundred articles in journals such as the Journal of the American Medical Association, the New England Journal of Medicine, Statistics in Medicine, and Controlled Clinical Trials.From the reviews:From the reviews:"A quick scan of the book indicates that it is not a typical statistics book…You can jumpin almost anywhere and just start reading…I like the book’s organization. There is a chapter on clinical trials. Then there are several chapters that explain the situations that arise from the occurrence of multiple analyses. Particular emphasis is given to multiple endpoints, situations where one continues a study to follow up on unanticipated results, and to subgroup analyses, interventions that impact only a fraction of the subjects in a study. The author is equally adept at describing clinical trials for the statistician as at explaining statistics to the clinical investigator. I enjoyed leafing through this book and would certainly enjoy have the opportunity to sit down and read it." Technometrics, August 2004"Moyé’s background as a statistician and MD makes him especially qualified to write this book…The clinical trial examples are a major strength of the book…His medical background and extensive clinical trials experience shine through." Statistics in Medicine, 2004, 23:3551-3559"The many examples from well known clinical trials are clearly one of the strengths of this book. It is also fascinating to share the author's experience with the FDA where he attended many meetings of Advisory Committees."Biometrics, December 2005"According to the preface, this book is written for clinical investigators and research groups within the pharmaceutical industry, medical students and regulators. … I admire the eloquency of the author. … The author does a remarkable job … . Without any doubt, the book is a valuable source of ideas for the intended audience. For statisticians it is an interesting source of experimental setups, that are actually used in practice and that consequently are worth while to be studied." (dr H. W. M. Hendriks, Kwantitatieve Methoden, Issue 72B41, 2005)"The book is entertaining and informative, sufficiently informal to recruit and retain the intended non-statisticalreadership, but sufficiently formal to detail methods. The author effectively sets up each issue with examples and conceptual discussion, grounding all in realistic examples." (T. A. Louis, Short Book Reviews, Vol. 24 (2), 2004)"Here in this book is a special situation for clinical trials in which the investigator must do statistical analyses for a number of different response measurements. … The author felt that the clinical investigators needed a book that they could understand. This book is his effort to reach that audience. … I like the book’s organization. … I enjoyed leafing through this book and would certainly enjoy having the opportunity to sit down and read it." (Eric R. Ziegel, Technometrics, Vol. 46 (3), August, 2004)"This book is written primarily for clinical researchers who are interested in designing and analysing clinical trials. It concentrates on elucidating problems arising from multiple analyses in clinical trials … . In general, the book is well written and easy to follow. … Although this book is written for clinical investigators, I find it a useful reference book for statisticians in the pharmaceutical industry … . Furthermore, senior statisticians may find the non-technical discussions and practical examples useful." (Shuying Yang, Pharmaceutical Statistics, Issue 3, 2004)"Without any doubt, the book is a valuable source of ideas for the intendend audience. For statisticians, it is an interesting source of experimental setups, that are actually used in practice and that consequently are worthwhile to be studied." (Dr. H.W.M. Hendriks, Kwantitatieve Methoden)