Book Description

The get-it-over-with-quickly approach to statistics has been encouraged - and often necessitated - by the short time allotted to it in most curriculums. If included at all, statistics is presented briefly, as a task to be endured mainly because pertinent questions may appear in subsequent examinations for licensure or other certifications. However, in later professional activities, clinicians and biomedical researchers will constantly be confronted with reports containing statistical expressions and analyses.Not just a set of cookbook recipes, Principles of Medical Statistics is designed to get you thinking about data and statistical procedures. It covers many new statistical methods and approaches like box plots, stem and leaf plots, concepts of stability, the bootstrap, and the jackknife methods of resampling. The book is arranged in a logical sequence that advances from simple to more elaborate results. The text describes all the conventional statistical procedures, and offers reasonably rigorous accounts of many of their mathematical justifications. Although the conventional mathematical principles are given a respectful account, the book provides a distinctly clinical orientation with examples and teaching exercises drawn from real world medical phenomena.Statistical procedures are an integral part of the basic background needed by biomedical researchers, students, and clinicians. Containing much more than most elementary texts, Principles of Medical Statistics fills the gap often found in the current curriculum. It repairs the imbalance that gives so little attention to the role of statistics as a prime component of basic biomedical education. ... Read more

Book Description

Each year hundreds of new drugs are approved for the marketplace. The approval of a single new drug is the result of years of screening tens of thousands of compounds, performing pre-clinical research on their effects, and designing, implementing, and analyzing the results of clinical trials. This book provides a general guide to statistical methods used in the pharmaceutical industry, and is aimed at graduate students and researchers who want to know more about statistical applications in all phases of the drug development process. The 19 chapters authored by over 30 statisticians working in the industry follow the general sequence of drug development, from pre-clinical research and saftey assessment to dose finding, safety studies, large clinical trials, analysis of health economic data, and fianlly manufacturing and production. Special topics such as single patient analysis and the impact of patient compliance show the broad spectrum of applications of data analysis methods. Each chapter illustrates a practical problem using data from actual studies by describing the study, the data, the methods, and the results. All of the analyses are done with S-PLUS, and the data and code are provided both in appendices to the chapters and on the companion Springer-Verlag web site, making it possible to reproduce the results and extend the analyses. Prior knowledge of the software is not required to follow the analyses. Steven P. Millard is a statistical consultant and also Manager of Consulting Services for MathSoft, Inc. (Data Analysis Products Division.) He has applied statistical methods to projects ranging from quality control in bioassay to measuring water quality to automating home appraisal, and has taught courses in S-PLUS for over 10 years. He is the author of the book Environmental Statistics with S-PLUS (CRC Press) as well as the S-PLUS add-in module ENVIRONMENTALSTATS for S-PLUS. Andreas Krause is a statistical consultant in the pharmaceutical industry and works for Novartis Pharma AG. He has extensive experience in graphical and numerical data analysis as well as programming, has taught numerous classes with and on S-PLUS, and is an S_PLUS user from the early days on. He is also author of the book The Basics of S and S-PLUS (Springer). ... Read more

Book Description

Public health deals with the prevention of disease, disability, and death in human populations. Statistics, the science of finding underlying patterns by analyzing variability and errors in collected data, is essential to understanding problems of disease in human populations because of widespread variation in social systems and biology. Other quantitative methods (e.g., economics, decision theory, deterministic mathematics) now form integral parts of the scientific basis of priority-setting and evaluation in public health.

This book gives broad conceptual treatment of the statistical issues underlying core public health functions: outbreak investigations, policy development, economic and program evaluation, managed care, and program operations. It will serve as a desk reference for public health practitioners and as a text for students of public health. ... Read more

Reviews (1)

Wrong title
This book is mis-titled, its about quantitative, not qualitative ... Read more

Book Description

A unique, unifying treatment for statistics and science in clinical trials

What sets this volume apart from the many books dealing with clinical trials is its integration of statistical and clinical disciplines. Stressing communication between biostatisticians and clinical scientists, this work clearly relates statistical interpretation to clinical issues arising in different stages of pharmaceutical research and development. Plus, the principles presented here are universal enough to be easily adapted in non-biopharmaceutical settings.

Design and Analysis of Clinical Trials tackles concepts and methodologies. It not only covers statistical basics such as uncertainty and bias, design considerations such as patient selection, randomization, and the different types of clinical trials but also deals with various methods of data analysis, group sequential procedures for interim analysis, efficacy data evaluation, analysis of safety data, and more. Throughout, the book:
* Surveys current and emerging clinical issues and newly developed statistical methods
* Presents a critical review of statistical methodologies in various therapeutic areas
* Features case studies from actual clinical trials
* Minimizes the mathematics involved, making the material widely accessible
* Offers each chapter as a self-contained entity
* Includes illustrations to highlight the text

This monumental reference on all facets of clinical trials is important reading for physicians, clinical and medical researchers, pharmaceutical scientists, clinical programmers, biostatisticians, and anyone involved in this burgeoning area of clinical research. It can also be used as a textbook in graduate-level courses in the field. ... Read more

Reviews (1)

good practical guide with FDA examples
The authors have experience conducting clinical trials in the pharmaceutical industry and this shows in their approach. All the issues that arise in an NDA submission to the FDA are covered. In the introduction the authors describe the regulatory process and the role of the FDA. They even provide organizational charts for the FDA Center for Drug Evaluation and Research. I have been working as a senior biostatistician for medical device companies for the last 5 years. Most of the trials I have worked on were regulated by the FDA and a number of issues that have been important to the FDA include (1) multiple comparisons, (2) intention-to-treat versus per protocol analysis, (3) pooling centers, (4) baseline data and demographics, (5) data monitoring and (6) safety. They are all covered in chapters 11 and 12 of this book.

Chapter 10 emphasizes sample size determination and interim analyses are covered in chapter 9. Randomization and blinding are covered in chapter 4. These topics are emphasized because of their importance in regulated clinical trials. One does not find them covered very much in other statistics texts on survival analysis or clinical trials.

The mathematics level is intermediate. The authors write well and incorporate the important practical interplay between the statistician, the clinician and the physician.

They provide many good references. The book is a good reference for anyone interested in clinical trials. Points are illustrated through the use of real trials.

Recent advances in Bayesian methods, resampling and meta analysis are not covered but most important topics are covered including group sequential methods. ... Read more

Book Description

The most important concept in statistics is the probability model. Only by fully understanding this model can one fully understand statistical analysis. Utilizing models in epidemiology, the authors of this self-contained account have chosen to emphasize the role of likelihood. This approach to statistics is both simple and intuitively satisfying. More complex problems can be tackled by natural extensions of the simple methods. This exploration of the statistical basis of epidemiology has been written specifically for professionals and graduate students in epidemiology, clinical epidemiology, or biostatistics. The simple prerequisite--basic training in biology--assumes no previous knowledge and the mathematics is deliberately kept at a manageable level. Based on a highly successful course by two internationally known authors, this book explains the essentials of statistics for all epidemiologists. ... Read more

Reviews (2)

Outstanding mongraph for biostatisticians
I find all other so-called biostatistics books are helpless
until I have read this book. This book is written based on
Clayton and Hills's past experience in applying statistics
in their studies. They can illustrate most difficult concepts
by very simple examples and words which are totally differnt
than other text and teaching books. For other books, you
can learn a lot of various statistical techniques, but you
cannot understand what is the core idea behind the statistics.
You may get it from this book.

A very good for the non-Mathematician
Clayton and Hill's book is an excellent introduction to the Statistics underlying epidemiology for readers who are not specialist Mathematicians. The book does explain fairly erudite ideas, but it does so gently and the Mathematics used is elementary. There are also many not-too-hard exercises at the ends of sections, making this a very gopd bok to learn from. Highly recommended. ... Read more

Book Description

This innovative, intermediate-level statistics text fills an important gap by presenting the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students. With an innovative approach, the author's introduces students to the mathematical and statistical concepts and tools that form a foundation for studying the theory and applications of both univariate and multivariate linear modelsA First Course in Linear Model Theory systematically presents the basic theory behind linear statistical models with motivation from an algebraic as well as a geometric perspective. Through the concepts and tools of matrix and linear algebra and distribution theory, it provides a framework for understanding classical and contemporary linear model theory. It does not merely introduce formulas, but develops in students the art of statistical thinking and inspires learning at an intuitive level by emphasizing conceptual understanding.The authors' fresh approach, methodical presentation, wealth of examples, and introduction to topics beyond the classical theory set this book apart from other texts on linear models. It forms a refreshing and invaluable first step in students' study of advanced linear models, generalized linear models, nonlinear models, and dynamic models. ... Read more

Reviews (1)

Excellent Introductory book
This book may serve as an excellent introductory tool to a graduate linear models class; specially for students who come from fields not all that related to math and statistics. The book starts with a broad review of linear algebra (it even mentions the forgotten tool of reduced row echelon form to find the rank of a matrix) followed by the theory of basic linear modelling. After the introduction, the book will gradually get deeper into the theory while implementing the concepts of the first few chapters. Some sections can be fairly complex which as a result makes the book appealing to both master and phD level students. The later chapters provide brief introductions to smoothing, longitudinal models, principal components and other concepts used in practice. There were some errors in the book but it is my understanding that most of them have been fixed in the second printing. My complements to the authors for such work. ... Read more

Book Description

Identifying the sources and measuring the impact of haphazard variations are important in any number of research applications, from clinical trials and genetics to industrial design and psychometric testing. Only in very simple situations can such variations be represented effectively by independent, identically distributed random variables or by random sampling from a hypothetical infinite population.Components of Variance illuminates the complexities of the subject, setting forth its principles with focus on both the development of models for detailed analyses and the statistical techniques themselves. The authors first consider balanced and unbalanced situations, then move to the treatment of non-normal data, beginning with the Poisson and binomial models and followed by extensions to survival data and more general situations. In the final chapter, they discuss ways of extending and assessing various models, including the study of exceedances, the use of nonlinear representations, the study of transformations of the response variable, and the detailed examination of the distributional form of the underlying random variables.Careful signposting and numerous examples from genetic data analysis, clinical trial design, longitudinal data analysis, industrial design, and meta-analysismake this book accessible - and valuable - not only to statisticians but to all applied research scientists who use statistical methods. ... Read more

Book Description

This practical resource provides well-integrated, comprehensive coverage of all the major statistical designs and methods used for animal studies in pharmaceutical research and development.Demonstrates the correct way to interpret the results of animal studies in the risk assessment of biopharmaceutical products!Clarifying detailed presentations with real-world examples, Design and Analysis of Animal Studies in Pharmaceutical Development furnishes definitions, background information, and regulatory requirements discusses statistical designs and methods for bioassay describes techniques for estimating drug efficacy and lethal doses illustrates statistical concepts as well as design and analysis principles for toxicology studies shows how to assess carcinogenicity and teratology for drugs under development details methods of analysis for in vitro Ames tests and Chinese hamster ovary cell mutagenesis studies and more! ... Read more

Book Description

A unique overview that melds the concepts of conditional probability and stochastic processes into real-life applications

The role of randomization techniques in clinical trials has become increasingly important. This comprehensive guide combines both the applied aspects of randomization in clinical trials with a probabilistic treatment of properties of randomization. Taking an unabashedly non-Bayesian and nonparametric approach to inference, the book focuses on the linear rank test under a randomization model, with added discussion on likelihood-based inference as it relates to sufficiency and ancillarity. Developments in stochastic processes and applied probability are also given where appropriate. Intuition is stressed over mathematics, but not without a clear development of the latter in the context of the former.

Providing a consolidated review of the field, the book includes relevant and practical discussions of:
* The benefits of randomization in terms of reduction of bias
* Randomization as a basis for inference
* Covariate-adaptive and response-adaptive randomization
* Current philosophies, controversies, and new developments

With ample problem sets, theoretical exercises, and short computer simulations using SAS, Randomization in Clinical Trials: Theory and Practice is equally useful as a standard textbook in biostatistics graduate programs as well as a reliable reference for biostatisticians in practice. ... Read more

Book Description

Let this down-to-earth book be your guide to the statistical integrity of your work. Without relying on the detailed and complex mathematical explanations found in many other statistical texts, Principles of Experimental Design for the Life Sciences teaches how to design, conduct, and interpret top-notch life science studies. Learn about the planning of biomedical studies, the principles of statistical design, sample size estimation, common designs in biological experiments, sequential clinical trials, high dimensional designs and process optimization, and the correspondence between objectives, design, and analysis. Each of these important topics is presented in an understandable and non-technical manner, free of statistical jargon and formulas. Written by a biostatistical consultant with 25 years of experience, Principles of Experimental Design for the Life Sciences is filled with real-life examples from the author's work that you can quickly and easily apply to your own. These examples illustrate the main concepts of experimental design and cover a broad range of application areas in both clinical and nonclinical research. With this one innovative, helpful book you can improve your understanding of statistics, enhance your confidence in your results, and, at long last, shake off those statistical shackles! ... Read more

Reviews (1)

A wonderful Guide for Designing Research Studies
This slim volume offers a great overview for how to design experimental research studies for the life sciences. It is relatively light on mathmatical formulas, but emphasizes the rational step by step approach one needs to take to plan out a study that will avoid the pitfalls of experimental design (or lack thereof) that can spell disaster for the researcher. The writing is understandable and straight forward. A number of practical examples are presented throughout the book which facilitate an understanding of the concepts presented. One key area which I especially appreciated was Selwyn's discusion of Power from the standpoint of sample size and how this can influence the selection of a particular experimental design.

In summary, this is an excellent text for classes which study scientific methods and design in the life sciences, and a good companion for a standard statistics text book. It is also great for investigators who want to conduct their research in a scientifically sound and efficient manner. ... Read more

Book Description

This is the ideal resource to review biostatistics and epidemiology for the USMLE Steps 1 and 2. Each chapter presents a case study, an explanation of the material and ends with a summary and review questions. ... Read more

Book Description

AIDS has appeared in more than 130 countries, and over 100,000 cases of AIDS have been reported in the U.S. alone.More and more, the public will be depending on statisticians to provide answers about the future course of this epidemic.

This comprehensive work confronts the problems that are unique to AIDS research and unites them under a single conceptual framework. It focuses on methods for the design and analysis of epidemiologic studies, the natural history of AIDS and the transmission of HIV, methods for tracking and projecting the course of the epidemic, and statistical issues in therapeutic trials.The various methods of monitoring and forecasting this disease receive comprehensive treatment.These methods include back-calculation, which the authors developed; interpretation of survey data on HIV prevalence; mathematical models for HIV transmission; and approaches that combine different types of epidemiological data. Much of this material -- such as a discussion of methods for assessing safety of the blood supply, an evaluation of survey approaches, and methods to project pediatric AIDS incidence -- is not available in any other work. ... Read more

Book Description

In 1948 the first randomized controlled trial was published by the English Medical Research Council in the British Medical Journal. Until then, observations had been uncontrolled. Initially, trials frequently did not confirm hypotheses to be tested. This phenomenon was attributed to low sensitivity due to small samples, as well as inappropriate hypotheses based on biased prior trials. Additional flaws were recognized and subsequently were better accounted for: carryover effects due to insufficient washout from previous treatments, time effects due to external factors and the natural history of the condition under study, bias due to asymmetry between treatment groups, lack of sensitivity due to a negative correlation between treatment responses, etc. Such flaws, mainly of a technical nature, have been largely corrected and led to trials after 1970 being of significantly better quality than before. The past decade has focused, in addition to technical aspects, on the need for circumspection in planning and conducting of clinical trials. As a consequence, prior to approval, clinical trial protocols are now routinely scrutinized by different circumstantial bodies, including ethics committees, institutional and federal review boards, national and international scientific organizations, and monitoring committees charged with conducting interim analyses.This book not only explains classical statistical analyses of clinical trials, but addresses relatively novel issues, including equivalence testing, interim analyses, sequential analyses, and meta-analyses, and provides a framework of the best statistical methods currently available for such purposes. The book is not only useful for investigators involved in the field of clinical trials, but also for all physicians who wish to better understand the data of trials as currently published. ... Read more