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Statistics for Technology: A Course in Applied Statistics, Third Edition (Chapman & Hall/CRC Texts in Statistical Science) Description

One of the most popular introductory texts in its field, Statistics for Technology: A Course in Applied Studies presents the range of statistical methods commonly used in science, social science, and engineering.
The mathematics are simple and straightforward; statistical concepts are explained carefully; and real-life (rather than contrived) examples are used throughout the chapters.
Divided into three parts, the Introduction describes some simple methods of summarizing data. Theory examines the basic concepts and theory of statistics. Applications covers the planning and procedures of experiments, quality control, and life testing.
Revised throughout, this Third Edition places a higher priority on the role of computers in analysis, and many new references have been incorporated. A new appendix describes general methods of tackling statistical problems, including guidance on literature searching and report writing.

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Most medical researchers, whether clinical or non-clinical, receive some background in statistics as undergraduates. However, it is most often brief, a long time ago, and largely forgotten by the time it is needed. Furthermore, many introductory texts fall short of adequately explaining the underlying concepts of statistics, and often are divorced from the reality of conducting and assessing medical research.

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From aspects of early trials to complex modeling problems, Advances in Clinical Trial Biostatistics summarizes current methodologies used in the design and analysis of clinical trials. Its chapters, contributed by internationally renowned methodologists experienced in clinical trials, address topics that include Bayesian methods for phase I clinical trials, adaptive two-stage clinical trials, and the design and analysis of cluster randomization trials, trials with multiple endpoints, and therapeutic equivalence trials. Other discussions explore Bayesian reporting, methods incorporating compliance in treatment evaluation, and statistical issues emerging from clinical trials in HIV infection.
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Building an up-to-date understanding of the methodologies that can be used to shape public health policies, Epidemiology: Study Design and Data Analysis, Second Edition encompasses the study of epidemiology from the observation of associations between risk factors and disease to the use of practical, data-supported analyses. It presents study designs commonly used for a wide range of purposes, and covers the spectrum of statistical principles and analytical tools used in epidemiological research, such as techniques used in report writing, descriptive analyses, statistical models and synthesis of evidence.

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The second edition of a popular textbook, this book emphasizes quantitative and design aspects of epidemiological research. The author favors the use of basic mathematics and practical methods over complicated mathematical proofs, making this an ideal textbook that is comprehensive yet accessible to graduate students in epidemiology, statistics, public health studies, and/or medical research.

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The CRC's Guide to Coordinating Clinical Research is a comprehensive training resource for investigative site staff. This invaluable guide offers CRCs the information they need to successfully coordinate a clinical trial from study startup to closeout and beyond.

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Cluster Randomised Trials discusses the design, conduct, and analysis of health trials that randomise groups of individuals to different treatments. It explores the advantages of cluster randomization, with special attention given to evaluating the effects of interventions against infectious diseases.

Avoiding unnecessary mathematical detail, the book covers basic concepts underlying the use of cluster randomisation, such as direct, indirect, and total effects. The authors also present an array of design issues in cluster randomised trials (CRTs), including strategies for minimizing contamination effects, the use of stratification and restricted randomisation to improve balance between treatment arms, special methods for sample size calculation, and alternatives to the simplest two-arm CRT. After covering analytical methods for CRTs, such as regression methods, the authors examine ethical issues, trial monitoring, interim analyses, reporting, and interpretation.

Although the book mainly focuses on medical and public health applications, it shows that the rigorous evidence of intervention effects provided by CRTs has the potential to inform public policy in a wide range of other areas. The book encourages readers to apply the methods to their own trials, reproduce the analyses presented, and explore alternative approaches.

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Introduction to Randomized Controlled Clinical Trials, Second Edition (Chapman & Hall/CRC Texts in Statistical Science) Description

Evidence from randomized controlled clinical trials is widely accepted as the only sound basis for assessing the efficacy of new medical treatments. Statistical methods play a key role in all stages of these trials, including their justification, design, and analysis. This second edition of Introduction to Randomized Controlled Clinical Trials provides a concise presentation of the principles applied in this area. It details the concepts behind randomization and methods for designing and analyzing trials and also includes information on meta-analysis and specialized designs, such as cross-over trials, cluster-randomized designs, and equivalence studies.

This latest edition features new and revised references, examples, exercises, and a new chapter dedicated to binary outcomes and survival analysis. It also presents numerous examples taken from the medical literature, contains exercises at the end of each chapter, and offers solutions in an appendix. The author uses Minitab and R software throughout the text for implementing the methods that are presented.

Comprehensive and accessible, Introduction to Randomized Controlled Clinical Trials is well-suited for those familiar with elementary statistical ideas and methods who want to further their knowledge of the subject.

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The third edition of the bestselling Clinical Trials in Oncology provides a concise, nontechnical, and thoroughly up-to-date review of methods and issues related to cancer clinical trials. The authors emphasize the importance of proper study design, analysis, and data management and identify the pitfalls inherent in these processes. In addition, the book has been restructured to have separate chapters and expanded discussions on general clinical trials issues, and issues specific to Phases I, II, and III. New sections cover innovations in Phase I designs, randomized Phase II designs, and overcoming the challenges of array data.

Although this book focuses on cancer trials, the same issues and concepts are important in any clinical setting. As always, the authors use clear, lucid prose and a multitude of real-world examples to convey the principles of successful trials without the need for a strong statistics or mathematics background. Armed with Clinical Trials in Oncology, Third Edition, clinicians and statisticians can avoid the many hazards that can jeopardize the success of a trial.

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Examines Critical Decisions for Transitioning Lab Science to a Clinical Setting
The development of therapeutic pharmaceutical compounds is becoming more expensive, and the success rates for getting such treatments approved for marketing and to the patients is decreasing. As a result, translational medicine (TM) is becoming increasingly important in the healthcare industry – a means of maximizing the consideration and use of information collected as compounds transition from initial lab discovery, through pre-clinical testing, early clinical trials, and late confirmatory studies that lead to regulatory approval of drug release to patients.

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Providing reliable information on an intervention effect, meta-analysis is a powerful statistical tool for analyzing and combining results from individual studies. Meta-Analysis of Binary Data Using Profile Likelihood focuses on the analysis and modeling of a meta-analysis with individually pooled data (MAIPD). It presents a unifying approach to modeling a treatment effect in a meta-analysis of clinical trials with binary outcomes.

After illustrating the meta-analytic situation of an MAIPD with several examples, the authors introduce the profile likelihood model and extend it to cope with unobserved heterogeneity. They describe elements of log-linear modeling, ways for finding the profile maximum likelihood estimator, and alternative approaches to the profile likelihood method. The authors also discuss how to model covariate information and unobserved heterogeneity simultaneously and use the profile likelihood method to estimate odds ratios. The final chapters look at quantifying heterogeneity in an MAIPD and show how meta-analysis can be applied to the surveillance of scrapie.

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Clinical trials have become essential research tools for evaluating the benefits and risks of new interventions for the treatment and prevention of diseases, from cardiovascular disease to cancer to AIDS. Based on the authors’ collective experiences in this field, Introduction to Statistical Methods for Clinical Trials presents various statistical topics relevant to the design, monitoring, and analysis of a clinical trial.

After reviewing the history, ethics, protocol, and regulatory issues of clinical trials, the book provides guidelines for formulating primary and secondary questions and translating clinical questions into statistical ones. It examines designs used in clinical trials, presents methods for determining sample size, and introduces constrained randomization procedures. The authors also discuss how various types of data must be collected to answer key questions in a trial. In addition, they explore common analysis methods, describe statistical methods that determine what an emerging trend represents, and present issues that arise in the analysis of data. The book concludes with suggestions for reporting trial results that are consistent with universal guidelines recommended by medical journals.

Developed from a course taught at the University of Wisconsin for the past 25 years, this textbook provides a solid understanding of the statistical approaches used in the design, conduct, and analysis of clinical trials.

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The first edition of Design and Analysis of Cross-Over Trials quickly became the standard reference on the subject and has remained so for more than 12 years. In that time, however, the use of cross-over trials has grown rapidly, particularly in the pharmaceutical arena, and researchers have made a number of advances in both the theory and methods applicable to these trials.

Completely revised and updated, the long-awaited second edition of this classic text retains its predecessor's careful balance of theory and practice while incorporating new approaches, more data sets, and a broader scope. Enhancements in the second edition include:

• A new chapter on bioequivalence
• Recently developed methods for analyzing longitudinal continuous and categorical data
• Real-world examples using the SAS system
• A comprehensive catalog of designs, datasets, and SAS programs available on a companion Web site at www.crcpress.com

The authors' exposition gives a clear, unified account of the design and analysis of cross-over trials from a statistical perspective along with their methodological underpinnings. With SAS programs and a thorough treatment of design issues, Design and Analysis of Cross-Over Trials, Second Edition sets a new standard for texts in this area and undoubtedly will be of direct practical value for years to come.

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Data collected on the time to an event-such as the death of a patient in a medical study-is known as survival data. The methods for analyzing survival data can also be used to analyze data on the time to events such as the recurrence of a disease or relief from symptoms. Modelling Survival Data in Medical Research begins with an introduction to survival analysis and a description of four studies in which survival data was obtained. These and other data sets are then used to illustrate the techniques presented in the following chapters, including the Cox and Weibull proportional hazards models; accelerated failure time models; models with time-dependent variables; interval-censored survival data; model checking; and use of statistical packages. Designed for statisticians in the pharmaceutical industry and medical research institutes, and for numerate scientists and clinicians analyzing their own data sets, this book also meets the need for an intermediate text which emphasizes the application of the methodology to survival data arising from medical studies.

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Now viewed as its own scientific discipline, clinical trial methodology encompasses the methods required for the protection of participants in a clinical trial and the methods necessary to provide a valid inference about the objective of the trial. Drawing from the authors’ courses on the subject as well as the first author’s more than 30 years working in the pharmaceutical industry, Clinical Trial Methodology emphasizes the importance of statistical thinking in clinical research and presents the methodology as a key component of clinical research.

From ethical issues and sample size considerations to adaptive design procedures and statistical analysis, the book first covers the methodology that spans every clinical trial regardless of the area of application. Crucial to the generic drug industry, bioequivalence clinical trials are then discussed. The authors describe a parallel bioequivalence clinical trial of six formulations incorporating group sequential procedures that permit sample size re-estimation. The final chapters incorporate real-world case studies of clinical trials from the authors’ own experiences. These examples include a landmark Phase III clinical trial involving the treatment of duodenal ulcers and Phase III clinical trials that contributed to the first drug approved for the treatment of Alzheimer’s disease.

Aided by the U.S. FDA, the U.S. National Institutes of Health, the pharmaceutical industry, and academia, the area of clinical trial methodology has evolved over the last six decades into a scientific discipline. This guide explores the processes essential for developing and conducting a quality clinical trial protocol and providing quality data collection, biostatistical analyses, and a clinical study report, all while maintaining the highest standards of ethics and excellence.

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More and more frequently, clinical trials include the evaluation of Health-Related Quality of Life (HRQoL), yet many investigators remain unaware of the unique measurement and analysis issues associated with the assessment of HRQoL. At the end of a study, clinicians and statisticians often face challenging and sometimes insurmountable analytic problems.

Design and Analysis of Quality of Life Studies in Clinical Trials details these issues and presents a range of solutions. Written from the author's extensive experience in the field, it focuses on the very specific features of QoL data: its longitudinal nature, multidimensionality, and the problem of missing data. The author uses three real clinical trials throughout her discussions to illustrate practical implementation of the strategies and analytic methods presented.

As Quality of Life becomes an increasingly important aspect of clinical trials, it becomes essential for clinicians, statisticians, and designers of these studies to understand and meet the challenges this kind of data present. In this book, SAS and S-PLUS programs, checklists, numerous figures, and a clear, concise presentation combine to provide readers with the tools and skills they need to successfully design, conduct, analyze, and report their own studies.