Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence (Wiley Series in Probability and Statistics) Best Reviews

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Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence acts as a source of basic methods for scientists wanting to combine evidence from different experiments. The authors aim to promote a deeper understanding of the notion of statistical evidence.

The book is comprised of two parts – The Handbook, and The Theory. The Handbook is a guide for combining and interpreting experimental evidence to solve standard statistical problems. This section allows someone with a rudimentary knowledge in general statistics to apply the methods. The Theory provides the motivation, theory and results of simulation experiments to justify the methodology.

This is a coherent introduction to the statistical concepts required to understand the authors’ thesis that evidence in a test statistic can often be calibrated when transformed to the right scale.

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Longitudinal data analysis for biomedical and behavioral sciences

This innovative book sets forth and describes methods for the analysis of longitudinaldata, emphasizing applications to problems in the biomedical and behavioral sciences. Reflecting the growing importance and use of longitudinal data across many areas of research, the text is designed to help users of statistics better analyze and understand this type of data.

Much of the material from the book grew out of a course taught by Dr. Hedeker on longitudinal data analysis. The material is, therefore, thoroughly classroom tested and includes a number of features designed to help readers better understand and apply the material. Statistical procedures featured within the text include:
* Repeated measures analysis of variance
* Multivariate analysis of variance for repeated measures
* Random-effects regression models (RRM)
* Covariance-pattern models
* Generalized-estimating equations (GEE) models
* Generalizations of RRM and GEE for categorical outcomes

Practical in their approach, the authors emphasize the applications of the methods, using real-world examples for illustration. Some syntax examples are provided, although the authors do not generally focus on software in this book. Several datasets and computer syntax examples are posted on this title's companion Web site. The authors intend to keep the syntax examples current as new versions of the software programs emerge.

This text is designed for both undergraduate and graduate courses in longitudinal data analysis. Instructors can take advantage of overheads and additional course materials available online for adopters. Applied statisticians in biomedicine and the social sciences can also use the book as a convenient reference.

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Best Biostatistical Methods: The Assessment of Relative Risks (Wiley Series in Probability and Statistics)

Biostatistical Methods: The Assessment of Relative Risks (Wiley Series in Probability and Statistics) by Wiley




Praise for the First Edition

". . . an excellent textbook . . . an indispensable reference for biostatisticians and epidemiologists."
—International Statistical Institute

A new edition of the definitive guide to classical and modern methods of biostatistics

Biostatistics consists of various quantitative techniques that are essential to the description and evaluation of relationships among biologic and medical phenomena. Biostatistical Methods: The Assessment of Relative Risks, Second Edition develops basic concepts and derives an expanded array of biostatistical methods through the application of both classical statistical tools and more modern likelihood-based theories. With its fluid and balanced presentation, the book guides readers through the important statistical methods for the assessment of absolute and relative risks in epidemiologic studies and clinical trials with categorical, count, and event-time data.

Presenting a broad scope of coverage and the latest research on the topic, the author begins with categorical data analysis methods for cross-sectional, prospective, and retrospective studies of binary, polychotomous, and ordinal data. Subsequent chapters present modern model-based approaches that include unconditional and conditional logistic regression; Poisson and negative binomial models for count data; and the analysis of event-time data including the Cox proportional hazards model and its generalizations. The book now includes an introduction to mixed models with fixed and random effects as well as expanded methods for evaluation of sample size and power. Additional new topics featured in this Second Edition include:

• Establishing equivalence and non-inferiority
• Methods for the analysis of polychotomous and ordinal data, including matched data and the Kappa agreement index
• Multinomial logistic for polychotomous data and proportional odds models for ordinal data
• Negative binomial models for count data as an alternative to the Poisson model
• GEE models for the analysis of longitudinal repeated measures and multivariate observations

Throughout the book, SAS is utilized to illustrate applications to numerous real-world examples and case studies. A related website features all the data used in examples and problem sets along with the author's SAS routines.

Biostatistical Methods, Second Edition is an excellent book for biostatistics courses at the graduate level. It is also an invaluable reference for biostatisticians, applied statisticians, and epidemiologists.Read more Biostatistical Methods: The Assessment of Relative Risks (Wiley Series in Probability and Statistics)

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*** Design and Analysis of Clinical Trials: Concept and Methodologies (Wiley Series in Probability and Statistics) reviews

Design and Analysis of Clinical Trials: Concept and Methodologies (Wiley Series in Probability and Statistics)

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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.

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Allied health professionals rely on Biostatistics for its high standards of statistical accuracy. It helps them develop a set of statistical tools that are relevant to their field. Now in its ninth edition, the book integrates new applications from several biological science fields throughout the pages. Each chapter now opens with bulleted objectives that highlight the main ideas. Summary boxes of formulae and statistical rules are presented for easy reference and review. Support is also provided for multiple programs such as SPSS, SAS, and STATA, in addition to Minitab. This includes screen captures and technology boxes with step-by-step help. Health professionals will then gain the ability to use technology to analyze data.

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Methods for Meta-Analysis in Medical Research (Wiley Series in Probability and Statistics - Applied Probability and Statistics Section) Best Reviews

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With meta-analysis methods playing a crucial role in health research in recent years, this important and clearly-written book provides a much-needed survey of the field.
Meta-analysis provides a framework for combining the results of several clinical trials and drawing inferences about the effectiveness of medical treatments. The move towards evidence-based health care and practice is underpinned by the use of meta-analysis. This book:
* Provides a thorough criticism and an up-to-date survey of meta-analysis methods
* Emphasises the practical approach, and illustrates the methods by numerous examples
* Describes the use of Bayesian methods in meta-analysis
* Includes discussion of appropriate software for each analysis
* Includes numerous references to more advanced treatment of specialist topics
* Refers to software code used in the examples available on the authors' Web site
Practising statisticians, statistically-minded clinicians and health research professionals will benefit greatly from the clear presentation and numerous examples. Medical researchers will grasp the basic principles of meta-analysis, and learn how to apply the various methods.

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Although few physicians, nurses, dentists, and other health professionals perform laboratory tests themselves, they all need to be able to interpret the results as well as understand findings reported in the medical literature. A general understanding of probability and statistics is essential for those needing to make daily decisions about the significance of research data, drug interaction precautions, or a patient's positive laboratory test for a rare disease.

Written with these needs in mind, Probability without Equations offers a thorough explanation of the subject without overwhelming the reader with equations and footnotes. Award-winning teacher Bart Holland presents a nontechnical treatment of intuitive concepts and presents numerous examples from medical research and practice. In plain language, this book explains the topics that clinicians need to understand:

• Analysis of variance

• "P-values" and the "t-test"

• Hazard models

• Regression and correlations

• Alpha and beta errors

"The Nobel prize-winning physicist Ernest Rutherford was fond of saying that if you need statistics to analyze the results of an experiment, you don't have a very good experiment. In a way he was right. However, a recurrent problem in medicine is that in a certain sense you commonly don't have a good experiment—but not because medical research scientists are generally incompetent! The nature of the data they work with is simply not as predictable as the data in some other fields, so the predictive nature of findings in medical science is generally rather imperfect."—from the introduction

Applied Survival Analysis: Regression Modeling of Time to Event Data (Wiley Series in Probability and Statistics) Reviews

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THE MOST PRACTICAL, UP-TO-DATE GUIDE TO MODELLING AND ANALYZING TIME-TO-EVENT DATA—NOW IN A VALUABLE NEW EDITION

Since publication of the first edition nearly a decade ago, analyses using time-to-event methods have increase considerably in all areas of scientific inquiry mainly as a result of model-building methods available in modern statistical software packages. However, there has been minimal coverage in the available literature to9 guide researchers, practitioners, and students who wish to apply these methods to health-related areas of study. Applied Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical, epidemiological, biostatistical, and other health-related research.

This book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. It offers a clear and accessible presentation of modern modeling techniques supplemented with real-world examples and case studies. Key topics covered include: variable selection, identification of the scale of continuous covariates, the role of interactions in the model, assessment of fit and model assumptions, regression diagnostics, recurrent event models, frailty models, additive models, competing risk models, and missing data.

Features of the Second Edition include:

• Expanded coverage of interactions and the covariate-adjusted survival functions
• The use of the Worchester Heart Attack Study as the main modeling data set for illustrating discussed concepts and techniques
• New discussion of variable selection with multivariable fractional polynomials
• Further exploration of time-varying covariates, complex with examples
• Additional treatment of the exponential, Weibull, and log-logistic parametric regression models
• Increased emphasis on interpreting and using results as well as utilizing multiple imputation methods to analyze data with missing values
• New examples and exercises at the end of each chapter

Analyses throughout the text are performed using Stata® Version 9, and an accompanying FTP site contains the data sets used in the book. Applied Survival Analysis, Second Edition is an ideal book for graduate-level courses in biostatistics, statistics, and epidemiologic methods. It also serves as a valuable reference for practitioners and researchers in any health-related field or for professionals in insurance and government.

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Main Features : Design and Analysis of Cross-Over Trials, Second Edition (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

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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Third Edition brings the text up to date with new material and updated references.

• New content includes an introduction to left and interval censored data; the log-logistic distribution; estimation procedures for left and interval censored data; parametric methods iwth covariates; Cox's proportional hazards model (including stratification and time-dependent covariates); and multiple responses to the logistic regression model.
• Coverage of graphical methods has been deleted.
• Large data sets are provided on an FTP site for readers' convenience.
• Bibliographic remarks conclude each chapter.

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Learn rigorous statistical methods to ensure valid clinical trials

This Second Edition of the critically hailed Clinical Trials builds on the text's reputation as a straightforward and authoritative presentation of statistical methods for clinical trials. Readers are introduced to the fundamentals of design for various types of clinical trials and then skillfully guided through the complete process of planning the experiment, assembling a study cohort, assessing data, and reporting results. Throughout the process, the author alerts readers to problems that may arise during the course of the trial and provides commonsense solutions.

The author bases the revisions and updates on his own classroom experience, as well as feedback from students, instructors, and medical and statistical professionals involved in clinical trials. The Second Edition greatly expands its coverage, ranging from statistical principles to controversial topics, including alternative medicine and ethics. At the same time, it offers more pragmatic advice for issues such as selecting outcomes, sample size, analysis, reporting, and handling allegations of misconduct. Readers familiar with the First Edition will discover completely new 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 key points. Revised discussion questions stimulate critical thinking and help readers understand how they can apply their newfound knowledge, and updated references are provided to direct readers to the most recent literature.

This text distinguishes itself with its accessible and broad coverage of statistical design methods--the crucial building blocks of clinical trials and medical research. Readers learn to conduct clinical trials that produce valid qualitative results backed by rigorous statistical methods.

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Praise for the First Edition

" . . . the book is a valuable addition to the literature in the field, serving as a much-needed guide for both clinicians and advanced students."—Zentralblatt MATH

A new edition of the cutting-edge guide to diagnostic tests in medical research

In recent years, a considerable amount of research has focused on evolving methods for designing and analyzing diagnostic accuracy studies. Statistical Methods in Diagnostic Medicine, Second Edition continues to provide a comprehensive approach to the topic, guiding readers through the necessary practices for understanding these studies and generalizing the results to patient populations.

Following a basic introduction to measuring test accuracy and study design, the authors successfully define various measures of diagnostic accuracy, describe strategies for designing diagnostic accuracy studies, and present key statistical methods for estimating and comparing test accuracy. Topics new to the Second Edition include:

• Methods for tests designed to detect and locate lesions

• Recommendations for covariate-adjustment

• Methods for estimating and comparing predictive values and sample size calculations

• Correcting techniques for verification and imperfect standard biases

• Sample size calculation for multiple reader studies when pilot data are available

• Updated meta-analysis methods, now incorporating random effects

Three case studies thoroughly showcase some of the questions and statistical issues that arise in diagnostic medicine, with all associated data provided in detailed appendices. A related web site features Fortran, SAS®, and R software packages so that readers can conduct their own analyses.

Statistical Methods in Diagnostic Medicine, Second Edition is an excellent supplement for biostatistics courses at the graduate level. It also serves as a valuable reference for clinicians and researchers working in the fields of medicine, epidemiology, and biostatistics.