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Contents Biostatistics Theory and Methods ........................3 Clinical Trials ......................................................11 Medical Biostatistics and Diagnostics..................15 Computational Biostatistics ................................18 Survival Analysis..................................................21 Page 6

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Statistical Genetics and Bioinformatics................22

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Biostatistics Theory and Methods New!

Analysis of Mixed Data Methods & Applications Edited by

Alexander R. de Leon University of Calgary, Alberta, Canada

Keumhee Carrière Chough University of Alberta, Edmonton, Canada

This is the first volume to date that offers a complete and up-to-date introduction to and summary of the fundamental advances and developments in the field of mixed models. It presents modern methods and extensively uses case studies throughout to illustrate interesting applications from economics, medicine and health, marketing, and genetics. All chapters include illustrative examples—many drawn from reallife case studies—and ample cross-references between chapters to enable readers to connect the book’s various topics and research strands as well as to facilitate self-study. • Includes contributions from major researchers on mixed data analysis

New!

Age-PeriodCohort Analysis New Models, Methods, and Empirical Applications Yang Yang University of North Carolina, Chapel Hill, USA

Kenneth C. Land Duke University, Durham, North Carolina, USA

This book explores the ways in which statistical models, methods, and research designs can be used to open new possibilities for APC analysis. Within a single, consistent HAPC-GLMM statistical modeling framework, the authors synthesize APC models and methods for three research designs: age-by-time period tables of population rates or proportions, repeated cross-section sample surveys, and accelerated longitudinal panel studies. They show how the empirical application of the models to various problems leads to many fascinating findings on how outcome variables develop along the age, period, and cohort dimensions.

• Presents a synthesis and development of future research directions

• Draws on the authors’ decade-long work on new models, methods, and empirical applications of APC analysis

• Includes terminology, methodologies, and statistical research applications

• Includes technical discussions of statistical issues and many empirical applications

Selected Contents:

• Illustrates the use of HAPC models for the aggregate population rates data design in cancer incidence and mortality

Analysis of Mixed Data: An Overview. Combining Univariate and Multivariate Random Forests for Enhancing Predictions of Mixed Outcomes. Joint Tests for Mixed Traits in Genetic Association Studies. Bias in Factor Score Regression and a Simple Solution. Joint Modeling of Mixed Count and Continuous Longitudinal Data. Factorization and Latent Variable Models for Joint Analysis of Binary and Continuous Outcomes. Regression Models for Analyzing Clustered Binary and Continuous Outcomes under the Assumption of Exchangeability. Random Effects Models for Joint Analysis of Repeatedly Measured Discrete and Continuous Outcomes. Hierarchical Modeling of Endpoints of Different Types with Generalized Linear Mixed Models. Joint Analysis of Mixed Discrete and Continuous Outcomes via Copula Models. Analysis of Mixed Outcomes in Econometrics: Applications in Health Economics. … Catalog no. K13979, January 2013, 262 pp. ISBN: 978-1-4398-8471-3, $89.95 / £57.99 Also available as an eBook

• Provides software, sample programs, and details on empirical analyses on the book’s web page

Selected Contents: Introduction. Why Cohort Analysis? APC Analysis of Data from Three Common Research Designs. Formalities of the Age-Period-Cohort Analysis Conundrum and a Generalized Linear Mixed Models (GLMM) Framework. APC Accounting/Multiple Classification Model, Part I: Model Identification and Estimation Using the Intrinsic Estimator. APC Accounting/Multiple Classification Model, Part II: Empirical Applications. Mixed Effects Models: Hierarchical APC-Cross-Classified Random Effects Models (HAPC-CCREM), Part I: The Basics. Mixed Effects Models: Hierarchical APC-Cross-Classified Random Effects Models (HAPC-CCREM), Part II: Advanced Analyses. … Catalog no. K14675, March 2013, c. 352 pp. ISBN: 978-1-4665-0752-4, $79.95 / £49.99 Also available as an eBook

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Biostatistics Theory and Methods Coming soon!

Coming soon!

Optimal Design for Nonlinear Response Models

Exercises and Solutions in Statistical Theory

Valerii V. Fedorov

Lawrence L. Kupper, Brian H. Neelon, and Sean M. O’Brien

GlaxoSmithKline, Collegeville, Pennsylvania, USA

This text is designed for teaching a course in statistical theory in a variety of disciplines, including statistics, biostatistics, mathematics, engineering, physics, computer science, and psychometrics.

AstraZeneca, Wilmington, Delaware, USA

The first section on probability and distribution theory contains exercises and selected solutions in probability theory, univariate distribution theory, and multivariate distribution theory. In the second section on statistical inference, the book contains exercises and selected solutions in estimation theory and hypothesis testing theory. The exercises vary in difficulty from basic to intermediate to advanced and solutions for about half the exercises are included. • Provides numerous exercises to supplement a course in statistical theory • Covers a wide range of topics, including correlated data analysis, latent class analysis, Bayesian analysis, measurement error, and multilevel modeling • Includes applications in medicine, epidemiology, actuarial science, social sciences, engineering, and genetics

Selected Contents: Concepts and Notation. Basic Probability Theory. Univariate Distribution Theory. Multivariate Distribution Theory. Estimation Theory. Hypothesis Testing Theory. Catalog no. K16626, May 2013, c. 390 pp. Soft Cover ISBN: 978-1-4665-7289-8, $59.95 / £38.99 Also available as an eBook

Sergei L. Leonov This book examines the theory of optimal modelbased design and provides examples of optimal designs for various models, mostly related to biopharmaceutical applications, such as dose-response studies. The authors pay special attention to adaptive or sequential optimal designs for nonlinear regression models when estimation and optimal design are performed in stages. Ideal for researchers in regression analysis and experimental design, the text illustrates optimal designs for various models using an example of the application of a first-order optimization algorithm in the space of information matrices, which is implemented in both MATLAB® and SAS. • Details the theory of optimal designs • Provides examples of models from biopharmaceutical science • Emphasizes adaptive or sequential optimal designs when estimation is performed in stages • Illustrates optimal designs for various models using implementations in MATLAB and SAS

Selected Contents: Regression Models and Their Analysis. Convex Design Theory. Algorithms and Numerical Techniques. Optimal Design under Constraints. Nonlinear Response Models. Locally Optimal Designs in Dose Finding. Examples of Optimal Designs in PK/PD Studies. Adaptive Model-Based Designs. Other Applications of Optimal Designs. Useful Matrix Formulae. Bibliography. Index. Catalog no. K11140, June 2013, c. 404 pp. ISBN: 978-1-4398-2151-0, $89.95 / £57.99 Also available as an eBook

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Biostatistics Theory and Methods New!

Generalized Estimating Equations Second Edition James W. Hardin University of South Carolina, Columbia, USA

Joseph M. Hilbe California Institute of Technology, Pasadena, and Arizona State University, Tempe, USA

Praise for the First Edition: “Generalized Estimating Equations is the first and only book to date dedicated exclusively to generalized estimating equations (GEE). … The authors do a good job of not only presenting the general theory of GEE models, but also giving explicit examples of various correlation structures, link functions, and a comparison between population-averaged and subject-specific models. … a valuable reference for researchers, teachers, and students who study and practice GLIM methodology.” —Journal of the American Statistics Association, March 2004

This second edition of a bestseller incorporates comments and suggestions from a variety of sources, including the Statistics.com course on longitudinal and panel models taught by the authors. Along with doubling the number of end-of-chapter exercises, this edition offers more thorough coverage of hypothesis testing and diagnostics, expands discussion of various models associated with GEE, and provides a new presentation of model selection procedures. Numerous examples are employed throughout the text, along with the software code used to create, run, and evaluate the models being examined. • Provides an overview of the theory and applications of GEEs • Adopts a practical approach with many examples • Contains new methods and examples • Includes theoretical details in the appendices • Offers Stata, SAS, and R code for the examples, with the code and data sets available online

Selected Contents: Introduction. Model Construction and Estimating Equations. Generalized Estimating Equations. Residuals, Diagnostics, and Testing. Programs and Datasets. References. Author Index. Subject Index. Catalog no. K13819, December 2012, 277 pp. ISBN: 978-1-4398-8113-2, $89.95 / £57.99

Generalized Linear Mixed Models Modern Concepts, Methods and Applications Walter W. Stroup University of Nebraska, Lincoln, USA

With numerous examples using SAS PROC GLIMMIX, this text presents an introduction to linear modeling using the generalized linear mixed model (GLMM) as an overarching conceptual framework. It shows how linear models fit with the rest of the core statistics curriculum and points out the major issues that statistical modelers must consider. • Provides a true introduction to linear modeling that assumes data need not be normally distributed and assumes random model effects to be the rule not an advanced exception • Emphasizes the connection between study design and all aspects of the model • Includes a chapter on GLMM-based power and sample size assessment—a critical tool for cost-effective design of research studies • Presents numerous examples using the SAS GLIMMIX procedure • Gives in-depth treatments of issues unique to generalized and mixed linear modeling, including conditional versus marginal modeling, broad versus narrow inference space, and data versus model-scale inference and reporting • Offers the data for all exercises as well as SAS files for all examples at www.crcpress.com

Selected Contents: PART I The Big Picture: Modeling Basics. Design Matters. Setting the Stage. PART II Estimation and Inference Essentials: Estimation. Inference, Part I: Model Effects. Inference, Part II: Covariance Components. PART III Working with GLMMs: Treatment and Explanatory Variable Structure. Multilevel Models. Best Linear Unbiased Prediction. Rates and Proportions. Counts. Time-to-Event Data. Multinomial Data. Correlated Errors, Part I: Repeated Measures. Correlated Errors, Part II: Spatial Variability. Power, Sample Size, and Planning. Appendices. References. Index. Catalog no. K10775, September 2012, 555 pp. ISBN: 978-1-4398-1512-0, $89.95 / £57.99 Also available as an eBook

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Biostatistics Theory and Methods

Stef van Buuren

Confidence Intervals for Proportions and Related Measures of Effect Size

TNO Quality of Life, Leiden, The Netherlands

Robert G. Newcombe Cardiff University, Wales

“It’s excellent and I highly recommend it. … van Buuren’s book is great even if you don’t end up using the algorithm described in the book … he supplies lots of intuition, examples, and graphs.”

This book illustrates the use of effect size measures and corresponding confidence intervals as more informative alternatives to the most basic and widely used significance tests. It provides you with a deep understanding of what happens when these statistical methods are applied in situations far removed from the familiar Gaussian case. Requiring little computational skills, the book offers user-friendly Excel spreadsheets for download at www.crcpress.com, enabling you to easily apply the methods to your own empirical data.

Flexible Imputation of Missing Data

—Andrew Gelman, Columbia University

“… a beautiful book that is so full of guidance for statisticians … exceptionally up to date and has more useful wisdom about dealing with common missing data problems than any other source I've seen.” —Frank Harrell, Vanderbilt University

“… a ‘no nonsense’ straightforward approach to the application of multiple imputation. I particularly like Stef’s use of graphical displays … I look forward to seeing more editions as this rapidly developing methodology continues to become even more effective at handling missing data problems in practice.” —Donald B. Rubin

This book provides a flexible and accessible framework for multiple imputation along with strategies for obtaining effective solutions to these problems. The text is supported by many examples using real data taken from the author’s vast research involving missing data. All of the analyses can be replicated in R using the dedicated package MICE. • Provides a practical guide to handling missing data • Examines various missing-data problems and presents strategies for tackling them • Enables readers to replicate the analyses and use the methods in their own work by providing software and other material on the author’s website

Selected Contents: Introduction. Basics: Multiple Imputation. Univariate Missing Data. Multivariate Missing Data. Imputation in Practice. Analysis of Imputed Data. Case Studies: Measurement Issues. Selection Issues. Longitudinal Data Issues. Extensions: Conclusion.

• Discusses the rationale for point and interval estimates as the mainstays of statistical inference • Develops and evaluates confidence intervals for a wide range of measures related to proportions • Presents an in-depth treatment of criteria for optimality and evaluation issues • Contains a wealth of application examples related to real-world research studies • Provides user-friendly computational resources on the book’s CRC Press web page

Selected Contents: Hypothesis Tests and Confidence Intervals. Means and Their Differences. Confidence Intervals for a Simple Binomial Proportion. Criteria for Optimality. Evaluation of Performance of Confidence Interval Methods. Intervals for the Poisson Parameter and the Substitution Approach. Difference between Independent Proportions and the Square-and-Add Approach. Difference between Proportions Based on Individually Paired Data. Methods for Triads of Proportions. Relative Risk and Rate Ratio. The Odds Ratio and Logistic Regression. Screening and Diagnostic Tests. Widening the Applicability of Confidence Interval Methods: The Propagating Imprecision Approach. Several Applications of the MOVER and PropImp Approaches. … Catalog no. K10649, August 2012, 468 pp. ISBN: 978-1-4398-1278-5, $89.95 / £57.99 Also available as an eBook

Catalog no. K13103, March 2012, 342 pp. ISBN: 978-1-4398-6824-9, $89.95 / £57.99 Also available as an eBook

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Biostatistics Theory and Methods Applied Categorical and Count Data Analysis Wan Tang, Hua He, and Xin M. Tu

Time Series Modeling of Neuroscience Data Tohru Ozaki

University of Rochester, New York, USA

Institute of Statistical Mathematics, Tokyo, Japan

This self-contained text explains how to perform the statistical analysis of discrete data. It covers classic concepts, popular topics, and modern areas that include models for zero-modified count outcomes and reliability analysis.

Due to recent advances in methodology that offer significant improvements over conventional methods, there is increasing interest in the use of time series models for the study of neuroscience data such as EEG, MEG, fMRI, and NIRS. This book presents an overview of time series models for the study of neuroscience data. Accessible to applied statisticians as well as quantitatively trained neuroscientists, the book is supported by many real examples to illustrate the methods. It provides useful instructions for computational problems, enabling readers to develop their own computational toolbox to apply the methods to real data.

• Shows how statistical models for noncontinuous responses are applied to real studies, emphasizing difficult and overlooked issues along the pathway from models to data • Reviews classic concepts and models for categorical data analysis • Offers an easy-to-follow presentation of modern concepts and approaches for count data, such as structural zeros and population mixtures

• Provides an overview of time series models for the study of neuroscience data

• Covers useful topics in modern-day clinical trials and observation studies, including longitudinal data analysis, measure scales, and counterfactual outcomes

• Illustrates the methods using real examples

• Presents a systematic treatment of instrumentation and measurement models for latent constructs, including measures of agreement and internal consistency

• Bridges the gap between complex mathematical models and applied science

• Compares popular models for clustered data, such as GLMM and GEE/WGEE

Introduction. Dynamic Models for Time Series Prediction: Time Series Prediction and the Power Spectrum. Discrete-Time Dynamic Models. Multivariate Dynamic Models. Continuous-Time Dynamic Models. Some More Models. Related Theories and Tools: Prediction and Doob Decomposition. Dynamics and Stationary Distributions. Bridge between Continuous-Time Models and Discrete-Time Models. Likelihood of Dynamic Models. State Space Modeling: Inference Problem (a) for State Space Models. Inference Problem (b) for State Space Models. Art of Likelihood Maximization. Causality Analysis. The New and Old Problems. References. Index.

• Gives an in-depth study of missing values and their impact on parametric and semiparametric (distribution-free) models • Includes exercises at the end of each chapter, many real data examples throughout, and sample programming codes in SAS, SPSS, and STATA for model implementations on a supporting website

Selected Contents: Introduction. Contingency Tables. Sets of Contingency Tables. Regression Models for Categorical Response. Regression Models for Count Response. Loglinear Models for Contingency Tables. Analyses of Discrete Survival Time. Longitudinal Data Analysis. Evaluation of Instruments. Analysis of Incomplete Data. References. Index.

• Supplies computational guidance on the application of the methods

Selected Contents:

Catalog no. C4602, January 2012, 574 pp. ISBN: 978-1-4200-9460-2, $99.95 / £63.99 Also available as an eBook

Catalog no. K10311, June 2012, 384 pp. ISBN: 978-1-4398-0624-1, $89.95 / £57.99 Also available as an eBook

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Biostatistics Theory and Methods

Nonparametric Statistical Tests A Computational Approach

Biostatistics A Computing Approach

Markus Neuhauser

Stewart Anderson

Koblenz University of Applied Sciences, Remagen, Germany

University of Pittsburgh, Pennsylvania, USA

“… there are substantial amounts of SAS code in the body of the work, and a briefer account of R code in an appendix. … While many standard statistical software packages include the classic nonparametric procedures, this volume presents many recent ones that have not found their way into most software yet … The writing is clear and concise … Highly recommended to anyone familiar with the classic nonparametric tests who wants an update (and extensive bibliography) concerning recent results.”

Focusing on visualization and computational approaches with an emphasis on the importance of simulation, this book introduces modern and classical biostatistical methods and compares their respective usefulness. It covers essential topics in biostatistical science, including regression, repeated measure, nonparametric analysis, survival analysis, sample size, and power calculations. Assuming only basic knowledge of probability and statistics, the text offers numerous practical applications, includes detailed worked examples taken from the medical area (all computed using R and SAS), and provides exercises with solutions.

—Robert W. Hayden, MAA Reviews, March 2012

Catalog no. K13006, December 2011, 248 pp. ISBN: 978-1-4398-6703-7, $93.95 / £59.99 Also available as an eBook

Catalog no. C8342, December 2011, 326 pp. ISBN: 978-1-58488-834-5, $83.95 / £41.99 Also available as an eBook

Handbook of Markov Chain Monte Carlo Edited by

Practical Multivariate Analysis

Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng

Fifth Edition Abdelmonem Afifi, Susanne May, and Virginia A. Clark

“I found this to be a remarkable book on the current state of MCMC methods in statistics. Any newcomer to the field will appreciate the thoughtful collection of articles … experts will find new aspects … a valuable reference book.”

“I found the text enjoyable and easy to read. The authors provide a sufficient description of all the methodology for practical use. Each chapter includes at least one real world dataset analysis and the software commands summary tables included at the end of every chapter should be particularly helpful to a practitioner of statistics. … I would recommend the text for practitioners of statistics looking for a handy reference, particularly those performing basic analysis in the health sciences.”

—Wolfgang Polasek, International Statistical Review, 2012

“The handbook provides a state-of-the-art view of a technology that has revolutionized contemporary model fitting. Researchers at all levels of familiarity with MCMC will find novel morsels of material to chew on.” —Alan E. Gelfand, Duke University

Catalog no. C7941, May 2011, 619 pp. ISBN: 978-1-4200-7941-8, $104.95 / £66.99

—Thomas J. Fisher, Journal of Biopharmaceutical Statistics, 2012

Catalog no. K10864, July 2011, 537 pp. ISBN: 978-1-4398-1680-6, $93.95 / £46.99 Also available as an eBook

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Biostatistics Theory and Methods Gaussian Process Regression Analysis for Functional Data

IntervalCensored Timeto-Event Data

Jian Qing Shi

Edited by

University of Newcastle upon Tyne, UK

Ding-Geng (Din) Chen, Jianguo Sun, and Karl E. Peace

Taeryon Choi Korea University, Seoul, South Korea

This work presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors cover the basics of Gaussian process regression models and methodological developments for high-dimensional data and variable selection. They also explore novel nonparametric statistical methods for curve prediction, curve clustering, functional ANOVA, and functional regression analysis of batch data, repeated curves, and non-Gaussian data. Some MATLAB® and C codes are available on the first author’s website.

Methods and Applications

This practical guide covers the latest developments in the analysis and modeling of interval-censored timeto-event data. Top researchers from academia, biopharmaceutical industries, and government agencies show how up-to-date statistical methods are used in biopharmaceutical and public health applications. The book presents data from actual clinical trials and biomedical research, including breast cancer and HIV data sets. It also offers easy access to computational methods and R software packages. Catalog no. K14515, July 2012, 433 pp. ISBN: 978-1-4665-0425-7, $99.95 / £63.99 Also available as an eBook

Catalog no. K11716, July 2011, 216 pp. ISBN: 978-1-4398-3773-3, $104.95 / £66.99 Also available as an eBook

Joint Models for Longitudinal and Time-toEvent Data With Applications in R

Event History Analysis with R

Dimitris Rizopoulos

Göran Broström

Erasmus University Medical Center, Rotterdam, Netherlands

Professor Emeritus, Umeå University, Sweden

This book introduces various extensions of the standard joint model, including several parameterizations for the association structure and the handling of competing risks. It covers diagnostic tools based on residuals to assess the assumptions behind a joint model, discusses dynamic predictions for survival and longitudinal outcomes, and presents discrimination concepts for longitudinal markers. The author emphasizes applications throughout so that readers understand the type of research questions best answered with joint models. All methods are implemented in R.

This book presents an introduction to survival and event history analysis. It includes a wide range of real examples of practical applications in demography, epidemiology, and econometrics, with data available online and supported by a dedicated R package for performing all of the analyses. Keeping mathematical details to a minimum, the author covers key topics, including both discrete and continuous time data, parametric proportional hazards, and accelerated failure times. He also describes how to use methods that are not available in other software for survival analysis.

Catalog no. K13371, June 2012, 275 pp. ISBN: 978-1-4398-7286-4, $79.95 / £49.99

Catalog no. K11534, April 2012, 236 pp. ISBN: 978-1-4398-3164-9, $79.95 / £49.99

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Biostatistics Theory and Methods

Analysis of Questionnaire Data with R

Statistical Thinking in Epidemiology

Bruno Falissard

Yu-Kang Tu and Mark S. Gilthorpe

INSERM U669, Paris, France

University of Leeds, UK

“The text covers many of the real-life concerns that arise when analyzing questionnaire data … . I recommend the book to any researchers and postgraduates embarking upon questionnaire design and analysis for the first time, especially in the field of social sciences.”

Addressing issues that have plagued researchers throughout the last decade, this book provides new insights into the many existing problems in statistical modeling and offers several alternative strategies to approach these problems. Emphasizing the importance of statistical thinking behind all analyses, the authors use specific examples in epidemiology to illustrate different model specifications that can imply different sets of causal relationships between variables. Each model is interpreted with regard to the context of implicit or explicit causal relationships.

—International Statistical Review, 2012

“I have found myself already referring to portions of the text as I consider various survey analyses, and I have recommended at least portions of it to students and colleagues. … an interesting and well-written book … .” —Ronald D. Fricker, Jr., Journal of Statistical Software, January 2012

Catalog no. K10018, July 2011, 231 pp. ISBN: 978-1-4200-9991-1, $93.95 / £59.99 Also available as an eBook

Catalog no. K10917, September 2011, 280 pp. ISBN: 978-1-4398-1766-7, $93.95 / £59.99 Also available as an eBook

New!

Bayesian Disease Mapping Hierarchical Modeling in Spatial Epidemiology, Second Edition Andrew B. Lawson Medical University of South Carolina, Charleston, USA

This second edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications. A biostatistics professor and WHO advisor, the author illustrates the use of Bayesian hierarchical modeling in the geographical analysis of disease through a range of real-world data sets. This edition includes new chapters on regression and ecological analysis, putative hazard modeling, and disease map surveillance as well as new appendices featuring examples of INLA and CAR models. It also presents expanded material on case event modeling and spatiotemporal analysis. Catalog no. K14543, March 2013, c. 392 pp. ISBN: 978-1-4665-0481-3, $89.95 / £57.99

The A-Z of Error-Free Research Phillip I. Good Consultant, Huntington Beach, California, USA

This practical book begins with an overview of when—and when not—to use statistics. It guides readers through the planning and data collection phases and presents various data analysis techniques, including methods for sample size determination. The author then covers techniques for developing models that provide a basis for future research. He also discusses reporting techniques to ensure research efforts get the proper credit. The book concludes with case control and cohort studies. R code is included to implement the methods. Catalog no. K14287, August 2012, 269 pp. Soft Cover ISBN: 978-1-4398-9737-9, $49.95 / £31.99

Also available as an eBook

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Clinical Trials Adaptive Design Methods in Clinical Trials Second Edition Shein-Chung Chow Duke University School of Medicine, Durham, North Carolina, USA

Mark Chang AMAG Pharmaceuticals, Inc, Lexington, Massachusetts, USA

“This second edition remains a useful reference source for anyone interested in advancing innovative trial designs and wishing to incorporate adaptations, modifications, and changes to the drug development process. Five new chapters have been added and are all worth reading … . For anyone working in and studying clinical research, the book is worth purchasing and will make a valuable addition to any library. …” —International Statistical Review, 2012

Catalog no. K11837, December 2011, 374 pp. ISBN: 978-1-4398-3987-4, $89.95 / £59.99

Coming soon!

Randomized Phase II Cancer Clinical Trials Sin-Ho Jung Duke University, Durham, North Carolina, USA

There has been a dramatic increase in the use of randomized phase II cancer clinical trials in recent years because of lower sample size requirements when multiple treatments are being evaluated. This accessible book covers both the latest developments in methodology as well as traditional single-arm phase II trial methods. Keeping the statistical level at a minimum, the book includes many diverse statistical designs and analysis methods relevant to oncology. Catalog no. K13295, May 2013, 230 pp. ISBN: 978-1-4398-7185-0, $89.95 / £57.99 Also available as an eBook

Also available as an eBook

Adaptive and Flexible Clinical Trials Richard Chin Institute for One World Health, San Francisco, California, USA

By enabling studies to be modified during the course of the trial, modern adaptive clinical trial designs can make studies substantially faster, more efficient, and more powerful than traditional clinical trials. The recent advances in web-based real-time data entry and novel statistical methods have made adaptive clinical trials practical and attractive. Suitable for readers involved in drug development, this is the first book that comprehensively explains all essential aspects of adaptive clinical trials. Without using highly technical statistical jargon, it discusses the design, conduct, and execution of these trials. Catalog no. K11738, August 2011, 198 pp. ISBN: 978-1-4398-3832-7, $93.95 / £59.99 Also available as an eBook

Design and Analysis of Bridging Studies Edited by

Jen-pei Liu, Shein-Chung Chow, and Chin-Fu Hsiao Taking into account the International Conference Harmonisation E5 framework for bridging studies, this book covers the regulatory requirements, scientific and practical issues, and statistical methodology for designing and evaluating bridging studies and multiregional clinical trials. For bridging studies, the authors explore ethnic sensitivity, the necessity of bridging studies, types of bridging studies, and the assessment of similarity between regions based on bridging evidence. For multiregional clinical trials, the text considers regional differences, assesses the consistency of treatment effect across regions, and discusses sample size determination for each region. Catalog no. K12075, July 2012, 287 pp. ISBN: 978-1-4398-4634-6, $99.95 / £63.99 Also available as an eBook

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Clinical Trials Handbook of Statistics in Clinical Oncology

Clinical Trials in Oncology Third Edition Stephanie Green, Jacqueline Benedetti, Angela Smith, and John Crowley Praise for the Previous Editions: “With over 60 years combined experience, the authors are ideally positioned to discuss the various statistical issues apparent in clinical trials, identifying alternative solutions and providing logical arguments for and against the various solutions. This book is also recommended for statisticians actively involved in the design, conduct, and analysis of clinical trial data (not only cancer clinical trials).” —Journal of Biopharmaceutical Statistics

“ALL medical oncology, radiation oncology, surgical oncology, and clinical research nurse academic training programs should provide this important text to trainees on Day 1.” —Charles R. Thomas Jr., University of Texas Health Science Center at San Antonio

Third Edition Edited by

John Crowley Cancer Research and Biostatistics, Seattle, Washington, USA

Antje Hoering Cancer Research and Biostatistics, University of Washington, and Fred Hutchinson Cancer Research Center, Seattle, Washington

Addressing the many challenges that have arisen since the publication of its predecessor, this third edition covers the newest developments involved in the design and analysis of cancer clinical trials. Accessible to statisticians in clinical trials as well as oncologists interested in clinical trial methodology, the book presents up-to-date statistical approaches for the design and analysis of oncology clinical trials. New topics in this edition include trial designs for targeted agents, Bayesian trial design, and the inclusion of high-dimensional data and imaging techniques.

Selected Contents:

Introduction. Statistical Concepts. The Design of Clinical Trials. Phase I and Phase I/II Trials. Phase II Trials. Phase III Trials. Data Management and Quality Control. Reporting of Results. Pitfalls. Exploratory Analyses. Summary and Conclusions. References. Index.

Choosing a Phase I Design. Dose Finding Designs Based on the Continual Reassessment Method. Pharmacokinetics in Clinical Oncology: Statistical Issues. Statistics of Phase 0 Trials. CRM Trials for Assessing Toxicity and Efficacy. Seamless Phase I/II Trial Design for Assessing Toxicity and Efficacy for Targeted Agents. Overview of Phase II Clinical Trials. Designs Based on Toxicity and Response. Designs Using Time to Event Endpoints/Single Arm versus Randomized Phase II. Phase II Selection Designs. Phase II with Multiple Subgroups: Designs Incorporating Disease Subtype or Genetic Heterogeneity. Phase II/III Designs. On Use of Covariates in Randomization and Analysis of Clinical Trials. Factorial Designs with Time to Event Endpoints. Early Stopping of Clinical Trials. Noninferiority Trials. Phase III Trials for Targeted Agents. Adaptive Trial Designs. Design of a Clinical Trial for Testing the Ability of a Continuous Marker to Predict Therapy Benefit. Software for Design and Analysis of Clinical Trials. Cure-Rate Survival Models in Clinical Trials. Design and Analysis of Quality of Life Data. Economic Analyses alongside Cancer Clinical Trials. Structural and Molecular Imaging in Cancer Therapy Clinical Trials. Prognostic Factor Studies. Predictive Modeling of Gene Expression Data. …

Catalog no. K10744, May 2012, 264 pp. ISBN: 978-1-4398-1448-2, $99.95 / £63.99

Catalog no. K12872, March 2012, 657 pp. ISBN: 978-1-4398-6200-1, $119.95 / £76.99

Also available as an eBook

This new edition of a bestseller provides a nontechnical and thoroughly up-to-date review of methods and issues relevant to clinical trials. The authors emphasize the importance of proper study design, analysis, and data management and identify the pitfalls inherent in these processes. The book offers 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. • Includes new material on Phase I designs, randomized Phase II designs, array data, and competing risks and subset analyses • Contains further comments on prognostic factor analysis • Describes complex exploratory analysis methods

Selected Contents:

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Clinical Trials Randomized Clinical Trials of Nonpharmacological Treatments Edited by

Design and Analysis of Non-Inferiority Trials

Isabelle Boutron, Philippe Ravaud, and David Moher

Mark D. Rothmann, Brian L. Wiens, and Ivan S.F. Chan

This book focuses on the methods of assessing nonpharmacological treatments, highlighting specific issues and discussing trial design. Providing practical examples that underline the issues and solutions, the book is one of the first to exclusively discuss various categories of treatments, from surgical procedures to psychotherapy.

“It is a pleasure to see a book completely devoted to the challenging arena of non-inferiority trials. … I am very impressed with its depth and breadth, and believe that it will be an important resource for anyone involved in designing non-inferiority trials. The authors weave in many examples, primarily in oncology, as well as a large set of references … a must-have resource for those involved in non-inferiority trials for the pharmaceutical industry and a must-read for those new to non-inferiority trials. A portion of a special topics course in a biostatistics department could be built around this book … .”

• Highlights specific issues in assessing nonpharmacological treatments in trials • Discusses all possible designs of trials for nonpharmacological treatments • Provides practical examples to highlight the issues and solutions in assessing nonpharmacological treatments • Considers various categories of treatments, including implantable devices, rehabilitation, and behavioral interventions

Selected Contents: Assessing Nonpharmacological Treatments: Theoretical Framework: Blinding in Nonpharmacological Randomized Controlled Trials. Placebo in Nonpharmacological Randomized Trials. Complexity of the Intervention. Learning Curves. Clustering Effects in RCTs of Nonpharmacological Interventions. Assessment of Harm. External Validity and Applicability of Nonpharmacological Trials. Assessing Nonpharmacological Interventions in Cluster Randomized Trials. Expertise-Based Trials. Pragmatic Trials and Nonpharmacological Evaluation. Preference Trials. Nonrandomized Studies to Evaluate the Effects of a Nonpharmacological Intervention. … Assessing Nonpharmacological Treatments: Practical Examples: Assessing Cardiothoracic Surgery: Practical Examples. Assessing Obstetrics and Gynecology: Practical Examples. Assessing Lower Limb Arthroplasty: Practical Examples. Assessing Radiation Therapy: Practical Examples. Assessing Electroconvulsive Therapy: Practical Examples. Assessing Acupuncture: Practical Examples. Assessing Orthosis: Practical Examples. Assessing Rehabilitation: Practical Examples. …

—Erica Brittain, Australian & New Zealand Journal of Statistics, May 2012

“… recommended for anyone working with clinical trials and in particular for those working in late phase drug development. It is an excellent source of concepts and statistical methods relevant for biostatisticians, clinical epidemiologists, and students. …” —Steffen Witte, Journal of Biopharmaceutical Statistics, 2012

• Discusses how to assess whether active control is effective and the size of a likely “random high” bias • Compares analysis methods with respect to the active control being selected based on outcomes • Presents the history of non-inferiority trials • Describes both Bayesian and frequentist methods as well as asymptotic and exact procedures

Selected Contents: What Is a Non-Inferiority Trial? Non-Inferiority Trial. Considerations. Strength of Evidence and Reproducibility. Evaluating the Active Control Effect. Across-Trials Analysis Methods. Three-Arm NonInferiority Trials. Multiple Comparisons. Missing Data and Analysis Sets. Safety Studies. Additional Topics. Inference on Proportions. Inferences on Means and Medians. Inference on Time-to-Event End Points. Appendix: Statistical Concepts. Index.

Catalog no. C8017, December 2011, 403 pp. ISBN: 978-1-4200-8801-4, $104.95 / £66.99

Catalog no. C8040, July 2011, 454 pp. ISBN: 978-1-58488-804-8, $93.95 / £59.99

Also available as an eBook

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Clinical Trials

Shein-Chung Chow

Dose Finding by the Continual Reassessment Method

Duke University School of Medicine, Durham, North Carolina, USA

Ying Kuen Cheung Columbia University, New York, New York, USA

“… wide ranging, covering all aspects of clinical trials, and has excellent links and references to regulatory aspects. … a useful reference work for clinical trials researchers.”

This book supplies practical, efficient dose-finding methods based on statistical research. More than just a cookbook, it provides full, unified coverage of the continual reassessment method (CRM) as well as stepby-step guidelines to the automation and parameterization of the methods used on a regular basis. The R package dfcrm is used to execute calibration techniques and perform simulations before a CRM is implemented in an actual trial

Controversial Statistical Issues in Clinical Trials

—David J. Hand, International Statistical Review, 2012

“I would recommend this book since it covers a number of areas that have not been covered in as much detail elsewhere. In particular, I thought the chapters on molecularly targeted therapies, follow-on biologics, multiregional clinical trials, and good statistical practices were well written and useful.”

• Examines critical issues that impact the clinical investigation of a test treatment

The author recognizes clinicians’ skepticism of modelbased designs and addresses the concerns that the time, professional, and computational resources necessary for accurate model-based designs can be major bottlenecks to the widespread use of appropriate dose-finding methods in phase I practice. The theoretically and empirically based methods in the book should lessen the statistician’s burden and encourage the continuing development and implementation of model-based dose-finding methods.

• Offers resolutions and recommendations that address the problems discussed

• Presents real clinical trial examples that illustrate the methods and techniques

• Gives examples of randomization/blinding, seamless trial design, various statistical tests, assessment of quality-of-life instruments, center grouping, clinical trial simulation, generalizability/reproducibility, and good review practices

• Details calibration techniques that enable biostatisticians to design a CRM in a timely manner

Selected Contents:

• Describes the use of R software to execute calibration techniques and perform simulations before a CRM is implemented in an actual trial

—William Mietlowski, Journal of Biopharmaceutical Statistics, 2012

• Identifies controversial statistical issues frequently encountered in clinical R&D

Introduction. Good Statistical Practices. Bench-toBedside Translational Research. Bioavailability and Bioequivalence. Hypotheses for Clinical Evaluation and Significant Digits. Instability of Sample Size Calculation. Integrity of Randomization/Blinding. Clinical Strategy for Endpoint Selection. Protocol Amendments. Seamless Adaptive Trial Designs. Multiplicity in Clinical Trials. Independence of Data Monitoring Committee. Two-Way ANOVA versus One-Way ANOVA with Repeated Measures. Validation of QOL Instruments. Missing Data Imputation. Center Grouping. Non-Inferiority Margin. QT Studies with Recording Replicates. Multiregional Clinical Trials. Dose Escalation Trials. …

• Outlines the limitations of the CRM to aid in the correct use of method

Selected Contents: Fundamentals. Design Calibration. CRM and Beyond. Catalog no. C9151, March 2011, 200 pp. ISBN: 978-1-4200-9151-9, $83.95 / £52.99 Also available as an eBook

Catalog no. K12247, June 2011, 611 pp. ISBN: 978-1-4398-4961-3, $99.95 / £66.99 Also available as an eBook

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Medical Biostatistics and Diagnostics New!

Regression Models as a Tool in Medical Research Werner Vach Institute of Medical Biometry and Medical Informatics, Freiburg, Germany

This text presents the fundamental concepts and important aspects of regression models most commonly used in medical research, including the classical regression model for continuous outcomes, the logistic regression model for binary outcomes, and the Cox proportional hazards model for survival data. The author emphasizes adequate use, correct interpretation of results, appropriate presentation of results, and avoidance of potential pitfalls. • Helps readers improve their understanding of the role of regression models in the medical field • Illustrates each technique with a concrete example, enabling readers to better appreciate the properties and theory of the methods • Uses Stata to demonstrate the practical use of the models • Discusses how and when regression models can fail • Describes the basic principles behind statistical computations, with more mathematical details given in the appendices • Offers the data sets, solutions to all exercises, and a short introduction to Stata on the author’s website

Selected Contents: THE BASICS. ADVANCED TOPICS AND TECHNIQUES: Some Useful Technicalities. Comparing Regression Coefficients. Power and Sample Size. The Selection of the Sample. The Selection of Covariates. Modeling Nonlinear Effects. Transformation of Covariates. Effect Modification and Interactions. Applying Regression Models to Clustered Data. Applying Regression Models to Longitudinal Data. The Impact of Measurement Error. The Impact of Incomplete Covariate Data. RISK SCORES AND PREDICTORS: Risk Scores. Construction of Predictors. Evaluating the Predictive Performance. Outlook: Construction of Parsimonious Predictors. MISCELLANEOUS: Alternatives to Regression Modeling. … MATHEMATICAL DETAILS. Bibliography. Index. Catalog no. K15111, November 2012, 495 pp. ISBN: 978-1-4665-1748-6, $89.95 / £57.99

New!

Bayesian Methods in Health Economics Gianluca Baio University College London, UK

This book provides an overview of Bayesian methods for the analysis of health economic data. After an introduction to the basic economic concepts and methods of evaluation, it presents Bayesian statistics using accessible mathematics. The next chapters describe the theory and practice of cost-effectiveness analysis from a statistical viewpoint and Bayesian computation, notably MCMC. The final chapter presents three detailed case studies covering cost-effectiveness analyses using individual data from clinical trials, evidence synthesis, hierarchical models, and Markov models. The text uses WinBUGS and JAGS, with data sets and code available online. • Provides an overview of Bayesian methods for cost-effectiveness analysis • Includes all the necessary background on economics and Bayesian statistics • Presents case studies of the cost-effectiveness analysis of health care interventions • Contains several worked examples that guide readers through the process of health economic evaluation • Covers the practice of making Bayesian analysis-integrating software, such as JAGS and R, specifically for the application of health economic analysis • Describes the methodological issues related to the application of Bayesian inference and decision process in health economics • Offers code to replicate the examples and an associated R package to produce systematic health economic evaluations of Bayesian models on the author’s website

Selected Contents: Introduction to Health Economic Evaluation. Introduction to Bayesian Inference. Statistical CostEffectiveness Analysis. Bayesian Analysis in Practice. Health Economic Evaluation in Practice. Catalog no. K14236, November 2012, 243 pp. ISBN: 978-1-4398-9555-9, $89.95 / £57.99 Also available as an eBook

Also available as an eBook

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Medical Biostatistics and Diagnostics

Statistics of Medical Imaging

Medical Biostatistics Third Edition Abhaya Indrayan

Tianhu Lei

The third edition of this acclaimed reference shows how biostatistics is a useful tool to manage many types of medical uncertainties. The author presents step-by-step explanations of statistical methods, along with numerous real-world examples and worked exercises. Guide charts at the beginning of the book enable quick access to the relevant statistical procedure.

Statistical investigation into technology not only provides a better understanding of the intrinsic features of the technology (analysis), but also leads to an improved design of the technology (synthesis). This book gives a theoretical framework for statistical investigation into medical technologies. Rather than offer detailed descriptions of statistics of basic imaging protocols of x-ray CT and MRI, the book presents a method to conduct similar statistical investigations into more complicated imaging protocols.

New to the Third Edition • New topics encompassing clinical trials with stopping rules, adaptive designs, sample size re-estimation and noninferiority margin, dietary indices, health inequality and ordinal association measures, Poisson distribution, various tests, meta-analysis, ridges and splines, path analysis, clinical agreement assessment, Six Sigma in health care, and much more • More detailed and expanded coverage of survival analysis, ROC curves, equivalence assessment, repeated measures ANOVA, and area under the concentration curve • Software illustrations of ANCOVA, repeated measures ANOVA, stepwise regression, quadratic regression, ROC curve, and survival analysis

Selected Contents: Medical Uncertainties. Basics of Medical Studies. Sampling Methods. Designs of Observational Studies. Medical Experiments. Clinical Trials. Numerical Methods for Representing Variation. Presentation of Variation by Figures. Some Quantitative Aspects of Medicine. Clinimetrics and Evidence-Based Medicine. Measurement of Community Health. Confidence Intervals, Principles of Tests of Significance, and Sample Size. Inference from Proportions. Relative Risk and Odds Ratio. Inference from Means. Relationships: Quantitative Data. Relationships: Qualitative Dependent. Survival Analysis. Simultaneous Consideration of Several Variables. Quality Considerations. Statistical Fallacies. Catalog no. K13952, August 2012, 1024 pp. ISBN: 978-1-4398-8414-0, $129.95 / £82.00 Also available as an eBook

• Describes the physical principles and mathematical procedures of medical imaging techniques • Presents statistical properties of imaging data (measurements) at each stage in the imaging processes of x-ray CT and MRI • Demonstrates image reconstruction as a transform from a set of random variables (imaging data) to another set of random variables (image data) • Discusses statistical properties of image data (pixel intensities) at three levels: a single pixel, any two pixels, and a group of pixels (a region) • Provides two stochastic models for x-ray CT and MR image in terms of their statistics and two model-based statistical image analysis methods • Evaluates statistical image analysis methods in terms of their detection, estimation, and classification performances • Indicates that x-ray CT, MRI, PET, and SPECT belong to a category of imaging: the nondiffraction CT

Selected Contents: Introduction. X-Ray CT Physics and Mathematics. MRI Physics and Mathematics. Non-Diffraction Computed Tomography. Statistics of X-Ray CT Imaging. Statistics of X-Ray CT Image. Statistics of MR Imaging. Statistics of MR Image. Stochastic Image Models. Statistical Image Analysis – I. Statistical Image Analysis – II. Performance Evaluation of Image Analysis Methods. Catalog no. C8842, December 2011, 438 pp. ISBN: 978-1-4200-8842-7, $99.95 / £63.99 Also available as an eBook

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Medical Biostatistics and Diagnostics Advanced Bayesian Methods for Medical Test Accuracy Lyle D. Broemeling Broemeling and Associates, Medical Lake, Washington, USA

After a review of the usual measures, including specificity, sensitivity, positive predictive value, negative predictive value, and the area under the ROC curve, this book expands its scope to cover the more advanced topics of verification bias, diagnostic tests with imperfect gold standards, and medical tests where no gold standard is available. The author offers a practical treatment by including R and WinBUGS code in the examples and by employing the Bayesian approach throughout the text. He also provides practical problems at the end of each chapter.

Statistical Evaluation of Diagnostic Performance Topics in ROC Analysis Kelly H. Zou, Aiyi Liu, Andriy I. Bandos, Lucila Ohno-Machado, and Howard E. Rockette

• Introduces advanced Bayesian methods for assessing the accuracy of medical test results

“This new book by Zou et al significantly contributes to the existing publications by providing short descriptions on basic issues and in-depth presentations on a few advanced, research-related issues. … the interested researcher can get inspired reading this book and discover new, unexplored research paths. Another pro of the book, useful for the interested researcher, is the extensive reference list at the end of each chapter. … a valuable starting point for those conducting basic research on ROC analysis and for applied researchers who are intrigued by the use of neat methodologies in applications.”

• Takes a very practical/computational approach through the use of both WinBUGS and R code in the examples

• Presents methods for the statistical validations of diagnostic accuracy using ROC analysis

• Uses regression techniques to estimate the effect of covariates on test accuracy

• Includes methods for estimating and comparing diagnostic test characteristics

• Emphasizes the methods for verification bias and methods for measuring accuracy without gold standards

• Covers monotone transformation methods, bi-normality test, and goodness-of-fit

• Provides many real-life examples from the author’s wide experience of working in a large medical center

Selected Contents: Introduction. Medical Tests and Preliminary Information. Preview of the Book. Fundamentals of Diagnostic Accuracy. Regression and Medical Test Accuracy. Agreement and Test Accuracy. Estimating Test Accuracy with an Imperfect Reference Standard. Verification Bias and Test Accuracy. Test Accuracy and Medical Practice. Accuracy of Combined Tests. Bayesian Methods for Meta-Analysis. Appendix: Introduction to WinBUGS. Catalog no. K11763, August 2011, 487 pp. ISBN: 978-1-4398-3878-5, $146.95 / £94.00 Also available as an eBook

—ISCB News, June 2012

• Describes Bayesian hierarchical models for estimating diagnostic accuracy • Discusses clustered and multireader and multimodality ROC and FROC analyses • Explores biomarkers, sequential designs, and bioinformatics

Selected Contents: Background and Introduction. Methods for Univariate and Multivariate Data: Diagnostic Rating Scales. Monotone Transformation Models. Combination and Pooling of Biomarkers. Bayesian ROC Methods. Advanced Approaches and Applications: Sequential Designs of ROC Experiments. Multireader ROC Analysis. FreeResponse ROC Analysis. Machine Learning and Predictive Modeling. Discussions and Extensions: Summary and Challenges. Section Appendices Symbols, Notations and Assumptions. Appendices. Index. Catalog no. K10617, July 2011, 245 pp. ISBN: 978-1-4398-1222-8, $93.95 / £59.99 Also available as an eBook

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Computational Biostatistics New!

Coming soon!

Handbook of SAS® DATA Step Programming Arthur Li Preparing a data set for analysis is perhaps the most important part of the entire data analysis process. Although there are many books on SAS programming, they often completely overlook, or have very brief coverage of, data manipulation and cleaning. Designed for novice users, this book gives a broad introduction to data step programming in SAS®. It focuses on understanding how the processes work, with detailed descriptions of the syntax. The text includes plenty of examples that illustrate the methods and give readers a better understanding of how to prepare their data for analysis in SAS. The book also provides exercises and offers code, data, solutions, and PowerPoint slides on www.crcpress.com. • Offers an accessible introduction to SAS data step programming • Presents lots of real examples to illustrate the topics • Includes exercises to enable use as a course text • Provides code and other material available for download on the book’s CRC Press web page

Geoff Der University of Glasgow, Scotland

Brian S. Everitt Professor Emeritus, King’s College London, UK

Designed for medical statisticians, this intermediatelevel reference explores the use of SAS for analyzing medical data. A chapter on visualizing data offers a detailed account of graphics for investigating data and smoothing techniques. The book also covers measurement in medicine, epidemiology/observational studies, meta-analysis, Bayesian methods, and handling missing data. The book maintains an example-based approach, with SAS code and output included throughout and available online. • Presents an accessible introduction to the analysis of medical data using SAS • Covers visualizing data, measurement, epidemiology, meta-analysis, Bayesian methods, and missing data • Provides SAS code and output to give step-bystep practical advice • Focuses on methods most commonly encountered in the analysis of medical data, including regression, longitudinal and survival data analysis, ANOVA and ANCOVA, and GAMs

Selected Contents:

Selected Contents: Introduction to SAS. Creating Variables Conditionally. The Essence of DATA Step Programming— Understanding How the PDV Works. The BY-Group Processing in the DATA Step. Writing Loops in the DATA Step. Array Processing. Combining Datasets. Data Input and Output. DATA Step Functions and CALL Routines. Useful SAS Procedures. Catalog no. K15213, April 2013, c. 280 pp. ISBN: 978-1-4665-5238-8, $59.95 / £38.99 Also available as an eBook

Applied Medical Statistics Using SAS

An Introduction to SAS. Statistics and Measurement in Medicine. Clinical Trials. Epidemiology. Metaanalysis. Analysis of Variance and Covariance. Scatter Plots, Correlation, Simple Regression, and Smoothing. Multiple Linear Regression. Logistic Regression. The Generalized Linear Model. Generalized Additive Models. The Analysis of Longitudinal Data I. The Analysis of Longitudinal Data II: Linear Mixed-Effects Models for Normal Response Variables. The Analysis of Longitudinal Data III: Non-Normal Responses. Survival Analysis. Cox’s Proportional Hazards Models for Survival Data. Bayesian Methods. Missing Values. Catalog no. K13087, October 2012, 559 pp. ISBN: 978-1-4398-6797-6, $89.95 / £57.99 Also available as an eBook

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Computational Biostatistics Practical Statistical Methods The R Primer

A SAS Programming Approach

Claus Thorn Ekstrøm

Lakshmi Padgett

University of Copenhagen, Frederiksberg, Denmark

Centocor, Malvern, Pennsylvania, USA

“This book provides a good introduction to R, using a clear layout and detailed, reproducible examples. An ideal tool for any new R user. … A wide range of topics are covered, making the book suitable for a variety of readers, from undergraduate students to professionals new to R. … an extremely helpful introduction to a very useful statistical package.”

This book presents a broad spectrum of statistical methods useful for researchers without an extensive statistical background. Omitting mathematical details and complicated formulae, it provides SAS programs to carry out the necessary analyses and draw appropriate inferences for common statistical problems.

—Claire Keeble, Journal of Applied Statistics, 2012

“… a nice starting point for learning R, and suitable for self-study provided the reader has some background in statistics.” —Olle Häggström, International Statistical Review, 2012

This primer provides a collection of concise examples and solutions to R problems frequently encountered by new users of this statistical software. Numerous examples illustrate a specific situation, topic, or problem, including data importing, data management, classical statistical analyses, and high-quality graphics production. Each example is self contained and includes R code that can be run exactly as shown, enabling results from the book to be replicated. Additional topics and R code are available from the book’s supporting website. • Presents concise examples and solutions to common problems in R • Explains how to read and interpret output from statistical analyses • Covers importing data, data handling, and creating graphics • Requires a basic understanding of statistics • Provides the R code used in the text on a supporting website

Selected Contents:

The author describes methods used for quantitative data and continuous data following normal and nonnormal distributions. She also focuses on simple linear regression, logistic regression, and the proportional hazards model. The final chapter briefly discusses such miscellaneous topics as propensity scores, misclassification errors, interim analysis, conditional power, bootstrap, and jackknife. • Explains concepts and interprets data using SAS outputs, avoiding complicated mathematical formulae • Covers many commonly used statistical methodologies, including multifactor ANOVA, nonparametric methods, Poisson regression, mixed models, and much more • Discusses related topics, such as diagnostic errors, jackknife estimators, bootstrap method, microarrays, group testing, multidimensional scaling, choice-based conjoint analysis, and meta-analysis

Selected Contents: Introduction. Qualitative Data. Continuous Normal Data. Nonparametric Methods. Regression. Miscellaneous Topics. References and Selected Bibliography. Index. Catalog no. K10634, April 2011, 304 pp. ISBN: 978-1-4398-1282-2, $83.95 / £52.99 Also available as an eBook

Importing Data. Manipulating Data. Statistical Analyses. Graphics. R. Bibliography. Index. Catalog no. K12876, August 2011, 299 pp. Soft Cover ISBN: 978-1-4398-6206-3, $39.95 / £26.99 Also available as an eBook

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Computational Biostatistics New!

Bayesian Analysis Made Simple An Excel GUI for WinBUGS Phil Woodward Pfizer, LTD, Sandwich, Kent, UK

“The author in writing this text has succeeded in making Bayesian analysis relatively simple through a graphical user interface (GUI) for WinBUGS— BugsXLA, which resides within Excel. … I recommend the book to anyone contemplating the use of Bayesian methods for the first time and already familiar with Excel … . The text provides an ideal introduction to Bayesian approaches using Excel and ultimately will encourage the reader to migrate to WinBUGS proper.” —International Statistical Review, 80, 2012

“… will help a competent statistician to run a Bayesian analysis of a generalized linear mixed model almost effortlessly.” —John Paul Gosling, Journal of Applied Statistics, 2012

• Shows how to integrate the power of Bayesian methods with the convenience that comes with using Excel to store and explore data • Provides numerous case studies that cover a vast range of model types and illustrate how to use BugsXLA to undertake an appropriate Bayesian analysis • Explains how even some of the more complex aspects of model specification can be routinely applied • Discusses current issues in the practical application of Bayesian methods, providing references for further study

Selected Contents: Brief Introduction to Statistics, Bayesian Methods, and WinBUGS. BugsXLA Overview and Reference Manual. Normal Linear Models (NLMs). Generalized Linear Models. Normal Linear Mixed Models. Generalized Linear Mixed Models. Emax or FourParameter Logistic Non-Linear Models. Bayesian Variable Selection. Longitudinal and Repeated Measures Models. Robust Models. Beyond BugsXLA: Extending the WinBUGS Code. Appendices. Catalog no. K11808, August 2011, 364 pp. ISBN: 978-1-4398-3954-6, $72.95 / £46.99

The BUGS Book A Practical Introduction to Bayesian Analysis David Lunn, Chris Jackson, Nicky Best, Andrew Thomas, and David Spiegelhalter “The most anticipated applied Bayesian text of the last 20 years, The BUGS Book is like a wonderful album by an established rock supergroup … the authors have created a masterpiece well worth the wait. The book offers the perfect mix of basic probability calculus, Bayes and MCMC basics, an incredibly broad array of useful statistical models, and a BUGS tutorial and user manual complete with all the ‘tricks’ one would expect from the team that invented the language. … provid[es] accessible yet comprehensive instruction in its proper use. A must-own for any working applied statistical modeler.” —Bradley P. Carlin, University of Minnesota

Authored by the team that developed the BUGS software, this text presents complete coverage of all the functionalities of BUGS, including prediction, missing data, model criticism, and prior sensitivity. It features a large number of worked examples, a wide range of applications from various disciplines, and numerous detailed exercises in every chapter. • Provides an accessible introduction to Bayesian analysis using the BUGS software • Covers all the functionalities of BUGS • Includes more exercises and solutions on a supporting website

Selected Contents: Introduction: Probability and Parameters. Monte Carlo Simulations using BUGS. Introduction to Bayesian Inference. Introduction to Markov Chain Monte Carlo Methods. Prior Distributions. Regression Models. Categorical Data. Model Checking and Comparison. Issues in Modeling. Hierarchical Models. Specialized Models. Different Implementations of BUGS. Appendices. Bibliography. Index. Catalog no. C8490, October 2012, 399 pp. Soft Cover ISBN: 978-1-58488-849-9, $49.95 / £24.99 Also available as an eBook

Also available as an eBook

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Survival Analysis Coming soon!

Multivariate Survival Analysis and Competing Risks Martin J. Crowder Imperial College, University of London, UK

Suitable for graduate students and researchers in statistics and biostatistics as well as those in the medical field, epidemiology, and social sciences, this book introduces univariate survival analysis and extends it to the multivariate case. It also covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods. • Provides a broad overview of multivariate survival analysis, competing risks, and counting processes • Contains many real-world examples to illustrate the methodology

Survival Analysis in Medicine and Genetics Jialiang Li National University of Singapore

Shuangge Ma Yale University, New Haven, Connecticut, USA

This text introduces up-to-date statistical methods for survival data analysis in medicine and genetics. Along with classical results, it presents new developments in interval censoring, statistical diagnostics with timedependent outcomes, analysis of ultra high-dimensional data sets, cure rate models, and repeated measure data. Suitable for both graduate students and biomedical researchers, the text covers applications in cancer studies, medical diagnosis, genetics, and genomics. It provides R code and example data sets online. Catalog no. K14175, June 2013, c. 384 pp. ISBN: 978-1-4398-9311-1, $99.95 / £63.99 Also available as an eBook

• Presents a clear style appropriate for graduate students in statistics • Offers a supporting R package for the analyses, with some code in the book

Selected Contents: Univariate Survival Analysis: Survival Data. Survival Distributions. Frailty Models. Parametric Methods. Discrete Time: Non- and Semi-Parametric Methods. Continuous-Time: Non- and Semi-Parametric Methods. Multivariate Survival Analysis: Multivariate Data and Distributions. Frailty and Copulas. Repeated Measure. Wear and Degradation. Competing Risks: Continuous Failure Times and Their Causes. Parametric Likelihood Inference. Latent Failure Times: Probability Distributions. Discrete Failure Times in Competing Risks. Hazard-Based Methods for Continuous Failure Times. Latent Failure Times: Identifiability Crises. Counting Processes in Survival Analysis: Some Basic Concepts. Survival Analysis. Non- and Semi-Parametric Methods. Catalog no. K13489, April 2012, 417 pp. ISBN: 978-1-4398-7521-6, $99.95 / £63.99 Also available as an eBook

Dynamic Prediction in Clinical Survival Analysis Hans van Houwelingen and Hein Putter Leiden University, The Netherlands

In the last 20 years, dynamic prediction models have been extensively used to monitor patient prognosis in survival analysis. Written by one of the pioneers in the area, this book synthesizes these developments in a unified framework. It covers a range of models, including prognostic and dynamic prediction of survival using genomic data and time-dependent information. The text includes numerous examples using real data taken from the authors’ collaborative research. R programs are provided for implementing the methods. Catalog no. K11593, November 2011, 250 pp. ISBN: 978-1-4398-3533-3, $93.95 / £59.99 Also available as an eBook

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Statistical Genetics and Bioinformatics Statistics and Data Analysis for Microarrays Using R and Bioconductor

Coming soon!

Introduction to Biological Networks

Second Edition

Alpan Raval

Sorin Drăghici

Claremont Graduate University, California, USA

Wayne State University, Detroit, Michigan, USA

This richly illustrated, bestselling text provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. It takes a hands-on, examplebased approach that explains the basics of R and microarray technology as well as how to choose and apply the proper data analysis tool to specific problems. Now using R and Bioconductor, this updated and greatly expanded edition includes 14 new chapters and offers the R code on a CD-ROM. • Presents an in-depth treatment of the statistical and data analysis aspects used in microarrays and bioinformatics • Provides the option of learning R in parallel with learning about data analysis • Covers background material for those with a limited mathematical, genetic, or molecular biology foundation

“Raval and Ray provide a comprehensive and modern exposition of a rapidly evolving field: network biology. This text will help biology, mathematics, and computer science students alike to become acquainted with the history and frontiers of research in this exciting area.” —Joshua B. Plotkin, University of Pennsylvania

“Finally a book has arrived that describes the basics of biological complexity. Written by leading scientists Raval and Ray, it provides a scholarly account of the concepts of network theory. It describes in great detail the experimental and computational methods for identifying and predicting biological networks and reveals how network analysis can be applied to solve fundamental questions in biology and medicine. Introduction to Biological Networks is easily the best read available on this important and rapidly developing field.” —Cornelis Murre, University of California-San Diego

Selected Contents: The Cell and Its Basic Mechanisms. Microarrays. Reliability and Reproducibility Issues in DNA Microarray Measurements. Image Processing. Introduction to R. Bioconductor: Principles and Illustrations. Elements of Statistics. Probability Distributions. Basic Statistics in R. Statistical Hypothesis Testing. Classical Approaches to Data Analysis. Analysis of Variance (ANOVA). Linear Models in R. Experiment Design. Multiple Comparisons. Analysis and Visualization Tools. Cluster Analysis. Quality Control. Data Pre-Processing and Normalization. Methods for Selecting Differentially Regulated Genes. The Gene Ontology (GO). Functional Analysis and Biological Interpretation of Microarray Data. Uses, Misuses, and Abuses in GO Profiling. A Comparison of Several Tools for Ontological Analysis. Focused Microarrays— Comparison and Selection. ID Mapping Issues. Pathway Analysis. Machine Learning Techniques. The Road Ahead. References. Catalog no. K10487, December 2011, 1036 pp. ISBN: 978-1-4398-0975-4, $89.95 / £57.99 Also available as an eBook

Animesh Ray

This book discusses the general principles behind network models and the essential concepts in the mathematical modeling of molecular regulatory networks in biology. It addresses computational methods for deriving network models from data. It also explores the testing of inferred networks by perturbation analysis on real biological systems using genomic techniques. • Highlights current progress in functional genomics and biological research • Integrates biological mechanisms using a bottom-up approach where genes and molecules are organized in complex networks • Relates abstract concepts in combinatorics and graph theory to questions in biology

Selected Contents: Introduction. Inferring Networks from Data. Testing Inferred Networks. Small Model Networks. Tractable Network Models. Discussion and Synthesis. Catalog no. C4630, April 2013, c. 328 pp. ISBN: 978-1-58488-463-7, $79.95 / £49.99 Also available as an eBook

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Exercises and Solutions in Statistical Theory • Provides a range of exercises to supplement a course in statistical theory • Covers correlated data analysis, latent class analysis, Bayesian analysis, measurement error, and multilevel modeling • Presents applications in medicine, epidemiology, actuarial science, social sciences, engineering, and genetics • Includes a solutions manual upon qualifying course adoption Catalog no. K16626, May 2013, c. 390 pp. Soft Cover, ISBN: 978-1-4665-7289-8 $59.95 / £38.99 Also available as an eBook

Survival Analysis in Medicine and Genetics • Shows students how to use various statistical methods for analyzing survival data • Provides a detailed introduction to newly developed methods for ultra highdimensional data in genetics and genomics • Offers data sets and computer programs online • Includes a solutions manual upon qualifying course adoption Catalog no. K14175, June 2013, c. 384 pp. ISBN: 978-1-4398-9311-1 $99.95 / £63.99 Also available as an eBook

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