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.

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.

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.

Concentrating on the rationale for the analyses, the difficulties posed by their interpretation, easily understood solutions, and useful problem sets, this book will help clinical investigators understand multiple analysis procedures and key issues. It 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.

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, Oa (TM)Briena "Fleming, and Lana "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

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.

Research in science has evolved, growing in breath and complexity. In genetics, agriculture, epidemiology, avionics, mathematics, biology, astronomy, economics, and medicine, the story in the same; experiments today are more complicated then they have ever been before. In the older paradigm that was in operation for hundreds of years, research efforts were crippled by the absence of technology, but propelled by competent, disciplined thought. Time was readily available for research design and personal development.
Today, technological demands eagerly consume our time as we take advantage of an ever expanding world of opportunity. If you permit it, your day is easily consumed with productive activities, e.g. electronic literature searches using a high speed internet connection at the airport, followed by an airborn grant writing session as you travel to yet one more meeting. Computers once had waiting lines of users; they now stand ready to word process, calculate, and even simulate experiments at our behest. The scientific systems can now accept more work product than we can produce. The faster we work, the faster the production of analyses, the quicker the generation of papers and products, the more rapid the pace of progress. In the old paradigm, travel and technological capacity were the rate limiting step. Now - we are.
This new dynamic places a greater premium on scientific productivity, yet there is more to a career than productivity. The principles, judgment, conduct, ethic, and temperament of researchers must develop simultaneously with their work product if these scientists are to develop into mature professionals. Although junior scientists have fine educationalbackgrounds, they frequently do not yet have the poise, vision, or coping skills that they need in order to identify and sustain the optimum productivity level in their careers. A philosophical approach that would help them achieve this balance would serve as an important foundation. However, junior scientists typically give little consideration to the development of a set of guiding principles. Often neglected, these researchers are left to stumble to this equipoise on their own. Unfortunately, many talented young scientists never find it, and can be confused, disoriented, and ultimately discouraged by their undirected search.
Finding Your Way In Science lays out for the scientist the principles that can produce and sustain the character growth that guides the development of the scientific professional.
The central thesis of Finding Your Way in Science is that the relentless pursuit of productivity is not a worthy career goal for the junior scientist. While productivity is and will be a fundamental attribute of the professional, there are other core themes that must be allowed to develop, appear, and exert their influences as well. The presence of self-control and patience, of moral excellence and compassion, of discipline and flexibility are as critical to the development of the junior scientist as is the acquisition of technical skills. The presence of these traits engenders collegiality, persuasive strength, responsibility, administrative diligence, influence, and vision, i.e. the qualities of charitable leadership.
Chapter One focuses on the need for the scientist to take stock of herself, carefully measuring her strengths and weaknesses. This chapter articulates thetheme that is the foundation of the book; productivity, so highly emphasized in academia, in private industry and in government is not the only star by which the scientist should steer. Chapter One delineates the dimensions of the scientist's character that must also be expanded in addition to the natural extension of knowledge that takes place at this time in their career. A broad overview of the role of the scientist's self-respect, ethics, sense of charity, and collegiality is provided. Concentrating on developing the strengths, skills, and outlook of a mature, professional scientist will not only amplify your productive efforts, but will also buffer and protect you as you face the unseen challenges that lie ahead. The specific scientific advances that your work produces will, in all likelihood, be overshadowed and surpassed by the future advances of others. However, the principles for which you stand as both a scientist and as an individual can resonate indefinitely.
Chapter Two discusses the function of data interpretation in research that is based on a sample from a much larger population. The role of the "surprise result" and the importance of result confirmation are provided in clear, non-mathematical language for the scientist. It is important for the scientist to recognize that they are not explorers or "searchers" but "researchers" and that their primary contribution is to provide confirmed scientific results that can be extended to larger populations.
The important of diligent administration is discussed in Chapter Three. This is a topic that many scientists shun because of its non-scientific nature and absence of direct scientific productivity. However, withoutthe development of skill in this area, the scientist runs the risk of inefficiency in his efforts as he struggles to identify and obtain the critical resources that he needs for his projects. The importance of mastering the logistics that researchers require to carry out their scientific developments is emphasized. Three focal points are identified that will help the scientist in his first role as principal investigator.
Chapter Four discusses the role of the scientist in collaborative projects. Being the junior member of a research team represents a fine opportunity to gain the experience and intuition that the scientist needs. The establishment of a clear sense of worth and self-value are necessary for any researcher involved in a productive collaborative efforts. Practical advice is provided for communicating with scientists in others fields w

This book 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). 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. 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.