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Contents

Biostatistics Theory and Methods ........................3

Clinical Trials ......................................................11

Medical Biostatistics and Diagnostics..................15

Computational Biostatistics ................................18

Survival Analysis..................................................21

Statistical Genetics and Bioinformatics................22Page 6 Page 7

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3

Biostatistics Theory and Methods

For more information and complete contents, visit www.crcpress.com

New!

Analysis ofMixed DataMethods &ApplicationsEdited by

Alexander R. de LeonUniversity of Calgary, Alberta,Canada

Keumhee Carrière ChoughUniversity of Alberta, Edmonton, Canada

This is the first volume to date that offers a completeand up-to-date introduction to and summary of thefundamental advances and developments in the fieldof mixed models. It presents modern methods andextensively uses case studies throughout to illustrateinteresting applications from economics, medicineand health, marketing, and genetics. All chaptersinclude illustrative examples—many drawn from real-life case studies—and ample cross-references betweenchapters to enable readers to connect the book’s var-ious topics and research strands as well as to facilitateself-study.

• Includes contributions from major researchers on mixed data analysis

• Presents a synthesis and development of futureresearch directions

• Includes terminology, methodologies, and statistical research applications

Selected Contents:

Analysis of Mixed Data: An Overview. CombiningUnivariate and Multivariate Random Forests forEnhancing Predictions of Mixed Outcomes. JointTests for Mixed Traits in Genetic Association Studies.Bias in Factor Score Regression and a SimpleSolution. Joint Modeling of Mixed Count andContinuous Longitudinal Data. Factorization andLatent Variable Models for Joint Analysis of Binaryand Continuous Outcomes. Regression Models forAnalyzing Clustered Binary and ContinuousOutcomes under the Assumption of Exchangeability.Random Effects Models for Joint Analysis ofRepeatedly Measured Discrete and ContinuousOutcomes. Hierarchical Modeling of Endpoints ofDifferent Types with Generalized Linear MixedModels. Joint Analysis of Mixed Discrete andContinuous Outcomes via Copula Models. Analysisof Mixed Outcomes in Econometrics: Applications inHealth Economics. …

Catalog no. K13979, January 2013, 262 pp.ISBN: 978-1-4398-8471-3, $89.95 / £57.99Also available as an eBook

New!

Age-Period-Cohort AnalysisNew Models,Methods, andEmpiricalApplicationsYang YangUniversity of North Carolina,Chapel Hill, USA

Kenneth C. LandDuke University, Durham, North Carolina, USA

This book explores the ways in which statistical models, methods, and research designs can be usedto open new possibilities for APC analysis. Within asingle, consistent HAPC-GLMM statistical modelingframework, the authors synthesize APC models andmethods for three research designs: age-by-time peri-od tables of population rates or proportions, repeatedcross-section sample surveys, and accelerated longitu-dinal panel studies. They show how the empiricalapplication of the models to various problems leads tomany fascinating findings on how outcome variablesdevelop along the age, period, and cohort dimen-sions.

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

• Includes technical discussions of statistical issues and many empirical applications

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

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

Selected Contents:

Introduction. Why Cohort Analysis? APC Analysis ofData from Three Common Research Designs.Formalities of the Age-Period-Cohort AnalysisConundrum and a Generalized Linear Mixed Models(GLMM) Framework. APC Accounting/MultipleClassification Model, Part I: Model Identification andEstimation Using the Intrinsic Estimator. APCAccounting/Multiple Classification Model, Part II:Empirical Applications. Mixed Effects Models:Hierarchical APC-Cross-Classified Random EffectsModels (HAPC-CCREM), Part I: The Basics. MixedEffects Models: Hierarchical APC-Cross-ClassifiedRandom Effects Models (HAPC-CCREM), Part II:Advanced Analyses. …

Catalog no. K14675, March 2013, c. 352 pp.ISBN: 978-1-4665-0752-4, $79.95 / £49.99Also available as an eBook



4 Save when you order online at www.crcpress.com


Coming soon!

Exercises andSolutions inStatisticalTheoryLawrence L. Kupper,Brian H. Neelon, andSean M. O’BrienThis text is designed for teaching a course in statisticaltheory in a variety of disciplines, including statistics,biostatistics, mathematics, engineering, physics, computer science, and psychometrics.

The first section on probability and distribution theory contains exercises and selected solutions inprobability theory, univariate distribution theory, andmultivariate distribution theory. In the second section on statistical inference, the book contains exercises and selected solutions in estimation theoryand hypothesis testing theory. The exercises vary indifficulty from basic to intermediate to advanced andsolutions for about half the exercises are included.

• Provides numerous exercises to supplement acourse 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. MultivariateDistribution Theory. Estimation Theory. HypothesisTesting Theory.

Catalog no. K16626, May 2013, c. 390 pp.Soft CoverISBN: 978-1-4665-7289-8, $59.95 / £38.99Also available as an eBook

Coming soon!

Optimal Designfor NonlinearResponseModelsValerii V. FedorovGlaxoSmithKline, Collegeville,Pennsylvania, USA

Sergei L. LeonovAstraZeneca, Wilmington,Delaware, USA

This book examines the theory of optimal model-based design and provides examples of optimaldesigns for various models, mostly related to biophar-maceutical applications, such as dose-response studies. The authors pay special attention to adaptiveor sequential optimal designs for nonlinear regressionmodels when estimation and optimal design are performed in stages.

Ideal for researchers in regression analysis and experi-mental design, the text illustrates optimal designs forvarious models using an example of the application ofa first-order optimization algorithm in the space ofinformation matrices, which is implemented in bothMATLAB® and SAS.

• Details the theory of optimal designs

• Provides examples of models from biopharmaceutical science

• Emphasizes adaptive or sequential optimaldesigns when estimation is performed in stages

• Illustrates optimal designs for various modelsusing implementations in MATLAB and SAS

Selected Contents:

Regression Models and Their Analysis. ConvexDesign Theory. Algorithms and NumericalTechniques. Optimal Design under Constraints.Nonlinear Response Models. Locally Optimal Designsin Dose Finding. Examples of Optimal Designs inPK/PD Studies. Adaptive Model-Based Designs.Other Applications of Optimal Designs. Useful MatrixFormulae. Bibliography. Index.

Catalog no. K11140, June 2013, c. 404 pp.ISBN: 978-1-4398-2151-0, $89.95 / £57.99Also available as an eBook



5



New!

GeneralizedEstimatingEquationsSecond EditionJames W. HardinUniversity of South Carolina,Columbia, USA

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

Praise for the First Edition:

“Generalized Estimating Equations is the first andonly book to date dedicated exclusively to general-ized estimating equations (GEE). … The authors doa good job of not only presenting the general theo-ry of GEE models, but also giving explicit examplesof various correlation structures, link functions, anda comparison between population-averaged andsubject-specific models. … a valuable reference forresearchers, teachers, and students who study andpractice 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 longitudinaland panel models taught by the authors. Along withdoubling the number of end-of-chapter exercises, thisedition offers more thorough coverage of hypothesistesting and diagnostics, expands discussion of variousmodels associated with GEE, and provides a new pres-entation of model selection procedures. Numerousexamples are employed throughout the text, alongwith the software code used to create, run, and eval-uate 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 EstimatingEquations. Generalized Estimating Equations.Residuals, Diagnostics, and Testing. Programs andDatasets. References. Author Index. Subject Index.

Catalog no. K13819, December 2012, 277 pp.ISBN: 978-1-4398-8113-2, $89.95 / £57.99Also available as an eBook

GeneralizedLinear MixedModelsModern Concepts,Methods andApplicationsWalter W. StroupUniversity of Nebraska, Lincoln,USA

With numerous examples using SAS PROC GLIMMIX,this text presents an introduction to linear modelingusing the generalized linear mixed model (GLMM) asan overarching conceptual framework. It shows howlinear models fit with the rest of the core statistics cur-riculum and points out the major issues that statisticalmodelers must consider.

• Provides a true introduction to linear modelingthat assumes data need not be normally distributed and assumes random model effectsto be the rule not an advanced exception

• Emphasizes the connection between studydesign and all aspects of the model

• Includes a chapter on GLMM-based power andsample size assessment—a critical tool for cost-effective design of research studies

• Presents numerous examples using the SASGLIMMIX procedure

• Gives in-depth treatments of issues unique togeneralized and mixed linear modeling, includ-ing conditional versus marginal modeling, broadversus narrow inference space, and data versusmodel-scale inference and reporting

• Offers the data for all exercises as well as SASfiles for all examples at www.crcpress.com

Selected Contents:

PART I The Big Picture: Modeling Basics. DesignMatters. Setting the Stage. PART II Estimation andInference Essentials: Estimation. Inference, Part I:Model Effects. Inference, Part II: CovarianceComponents. 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: RepeatedMeasures. 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 / £57.99Also available as an eBook





FlexibleImputation ofMissing DataStef van BuurenTNO Quality of Life, Leiden, The Netherlands

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

—Andrew Gelman, Columbia University

“… a beautiful book that is so full of guidance forstatisticians … exceptionally up to date and hasmore useful wisdom about dealing with commonmissing data problems than any other source I'veseen.”

—Frank Harrell, Vanderbilt University

“… a ‘no nonsense’ straightforward approach tothe application of multiple imputation. I particular-ly like Stef’s use of graphical displays … I look forward to seeing more editions as this rapidly developing methodology continues to become evenmore effective at handling missing data problems inpractice.”

—Donald B. Rubin

This book provides a flexible and accessible frame-work for multiple imputation along with strategies forobtaining effective solutions to these problems. Thetext is supported by many examples using real datataken from the author’s vast research involving miss-ing data. All of the analyses can be replicated in Rusing the dedicated package MICE.

• Provides a practical guide to handling missingdata

• Examines various missing-data problems andpresents strategies for tackling them

• Enables readers to replicate the analyses and usethe methods in their own work by providingsoftware and other material on the author’swebsite

Selected Contents:

Introduction. Basics: Multiple Imputation. UnivariateMissing Data. Multivariate Missing Data. Imputationin Practice. Analysis of Imputed Data. Case Studies:Measurement Issues. Selection Issues. LongitudinalData Issues. Extensions: Conclusion.

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

ConfidenceIntervals forProportions and RelatedMeasures ofEffect SizeRobert G. NewcombeCardiff University, Wales

This book illustrates the use of effect size measuresand corresponding confidence intervals as moreinformative alternatives to the most basic and widelyused significance tests. It provides you with a deepunderstanding of what happens when these statisticalmethods are applied in situations far removed fromthe familiar Gaussian case. Requiring little computa-tional skills, the book offers user-friendly Excel spread-sheets for download at www.crcpress.com, enablingyou to easily apply the methods to your own empiri-cal data.

• Discusses the rationale for point and intervalestimates as the mainstays of statistical inference

• Develops and evaluates confidence intervals fora wide range of measures related to proportions

• Presents an in-depth treatment of criteria foroptimality and evaluation issues

• Contains a wealth of application examples related to real-world research studies

• Provides user-friendly computational resourceson the book’s CRC Press web page

Selected Contents:

Hypothesis Tests and Confidence Intervals. Meansand Their Differences. Confidence Intervals for aSimple Binomial Proportion. Criteria for Optimality.Evaluation of Performance of Confidence IntervalMethods. Intervals for the Poisson Parameter and theSubstitution Approach. Difference betweenIndependent Proportions and the Square-and-AddApproach. Difference between Proportions Based onIndividually Paired Data. Methods for Triads ofProportions. Relative Risk and Rate Ratio. The OddsRatio and Logistic Regression. Screening andDiagnostic Tests. Widening the Applicability ofConfidence Interval Methods: The PropagatingImprecision Approach. Several Applications of theMOVER and PropImp Approaches. …

Catalog no. K10649, August 2012, 468 pp.ISBN: 978-1-4398-1278-5, $89.95 / £57.99Also available as an eBook



7



AppliedCategorical andCount DataAnalysisWan Tang, Hua He, andXin M. TuUniversity of Rochester, New York, USA

This self-contained text explains how to perform thestatistical analysis of discrete data. It covers classic concepts, popular topics, and modern areas thatinclude models for zero-modified count outcomesand reliability analysis.

• Shows how statistical models for noncontinuousresponses are applied to real studies, emphasiz-ing difficult and overlooked issues along thepathway from models to data

• Reviews classic concepts and models for categorical data analysis

• Offers an easy-to-follow presentation of modernconcepts and approaches for count data, suchas structural zeros and population mixtures

• Covers useful topics in modern-day clinical trialsand observation studies, including longitudinaldata analysis, measure scales, and counterfactualoutcomes

• Presents a systematic treatment of instrumenta-tion and measurement models for latent constructs, including measures of agreementand internal consistency

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

• Gives an in-depth study of missing values andtheir 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, andSTATA for model implementations on a supporting website

Selected Contents:

Introduction. Contingency Tables. Sets ofContingency Tables. Regression Models forCategorical Response. Regression Models for CountResponse. Loglinear Models for Contingency Tables.Analyses of Discrete Survival Time. Longitudinal DataAnalysis. Evaluation of Instruments. Analysis ofIncomplete Data. References. Index.

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

Time SeriesModeling ofNeuroscienceDataTohru OzakiInstitute of StatisticalMathematics, Tokyo, Japan

Due to recent advances in methodology that offer sig-nificant improvements over conventional methods,there is increasing interest in the use of time seriesmodels for the study of neuroscience data such asEEG, MEG, fMRI, and NIRS. This book presents anoverview of time series models for the study of neuro-science data. Accessible to applied statisticians as wellas quantitatively trained neuroscientists, the book issupported by many real examples to illustrate themethods. It provides useful instructions for computa-tional problems, enabling readers to develop theirown computational toolbox to apply the methods toreal data.

• Provides an overview of time series models forthe study of neuroscience data

• Illustrates the methods using real examples

• Supplies computational guidance on the application of the methods

• Bridges the gap between complex mathematical models and applied science

Selected Contents:

Introduction. Dynamic Models for Time SeriesPrediction: Time Series Prediction and the PowerSpectrum. Discrete-Time Dynamic Models.Multivariate Dynamic Models. Continuous-TimeDynamic Models. Some More Models. RelatedTheories and Tools: Prediction and DoobDecomposition. Dynamics and StationaryDistributions. Bridge between Continuous-TimeModels and Discrete-Time Models. Likelihood ofDynamic Models. State Space Modeling: InferenceProblem (a) for State Space Models. InferenceProblem (b) for State Space Models. Art of LikelihoodMaximization. Causality Analysis. The New and OldProblems. References. Index.

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





NonparametricStatistical TestsA ComputationalApproachMarkus NeuhauserKoblenz University of AppliedSciences, Remagen, Germany

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

—Robert W. Hayden, MAA Reviews, March 2012


BiostatisticsA ComputingApproachStewart AndersonUniversity of Pittsburgh,Pennsylvania, USA

Focusing on visualization and computationalapproaches with an emphasis on the importance ofsimulation, this book introduces modern and classicalbiostatistical methods and compares their respectiveusefulness. It covers essential topics in biostatistical sci-ence, including regression, repeated measure, non-parametric analysis, survival analysis, sample size, andpower calculations. Assuming only basic knowledgeof probability and statistics, the text offers numerouspractical applications, includes detailed worked exam-ples taken from the medical area (all computed usingR and SAS), and provides exercises with solutions.

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

Handbook ofMarkov ChainMonte CarloEdited by

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

“I found this to be a remarkable book on the currentstate of MCMC methods in statistics. Any newcomerto the field will appreciate the thoughtful collectionof articles … experts will find new aspects … a valu-able reference book.”

—Wolfgang Polasek, International Statistical Review,2012

“The handbook provides a state-of-the-art view of atechnology that has revolutionized contemporarymodel fitting. Researchers at all levels of familiaritywith MCMC will find novel morsels of material tochew on.”

—Alan E. Gelfand, Duke University

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

PracticalMultivariateAnalysisFifth EditionAbdelmonem Afifi,Susanne May, andVirginia A. Clark

“I found the text enjoyable and easy to read. Theauthors provide a sufficient description of all themethodology for practical use. Each chapterincludes at least one real world dataset analysis andthe software commands summary tables included atthe end of every chapter should be particularly help-ful to a practitioner of statistics. … I would recom-mend the text for practitioners of statistics lookingfor a handy reference, particularly those performingbasic analysis in the health sciences.”

—Thomas J. Fisher, Journal of BiopharmaceuticalStatistics, 2012

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





9



GaussianProcessRegressionAnalysis forFunctional DataJian Qing ShiUniversity of Newcastle uponTyne, UK

Taeryon ChoiKorea University, Seoul, South Korea

This work presents nonparametric statistical methodsfor functional regression analysis, specifically themethods based on a Gaussian process prior in a func-tional space. The authors cover the basics of Gaussianprocess regression models and methodological devel-opments for high-dimensional data and variable selec-tion. They also explore novel nonparametric statisticalmethods for curve prediction, curve clustering, func-tional ANOVA, and functional regression analysis ofbatch data, repeated curves, and non-Gaussian data.Some MATLAB® and C codes are available on the firstauthor’s website.


Interval-Censored Time-to-Event DataMethods andApplicationsEdited by

Ding-Geng (Din) Chen,Jianguo Sun, and Karl E. PeaceThis practical guide covers the latest developments inthe analysis and modeling of interval-censored time-to-event data. Top researchers from academia, bio-pharmaceutical industries, and government agenciesshow how up-to-date statistical methods are used inbiopharmaceutical and public health applications.The book presents data from actual clinical trials andbiomedical research, including breast cancer and HIVdata sets. It also offers easy access to computationalmethods and R software packages.


Joint Models forLongitudinaland Time-to-Event DataWith Applications in RDimitris RizopoulosErasmus University MedicalCenter, Rotterdam, Netherlands

This book introduces various extensions of the stan-dard joint model, including several parameterizationsfor the association structure and the handling of competing risks. It covers diagnostic tools based onresiduals to assess the assumptions behind a jointmodel, discusses dynamic predictions for survival andlongitudinal outcomes, and presents discriminationconcepts for longitudinal markers. The author empha-sizes applications throughout so that readers under-stand the type of research questions best answeredwith joint models. All methods are implemented in R.


Event HistoryAnalysis with RGöran BroströmProfessor Emeritus, UmeåUniversity, Sweden

This book presents an introduction to survival andevent history analysis. It includes a wide range of realexamples of practical applications in demography,epidemiology, and econometrics, with data availableonline and supported by a dedicated R package forperforming all of the analyses. Keeping mathematicaldetails to a minimum, the author covers key topics,including both discrete and continuous time data,parametric proportional hazards, and accelerated fail-ure times. He also describes how to use methods thatare not available in other software for survival analysis.

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







Analysis ofQuestionnaireData with RBruno FalissardINSERM U669, Paris, France

“The text covers many of the real-life concerns thatarise when analyzing questionnaire data … . I rec-ommend the book to any researchers and post-graduates embarking upon questionnaire designand analysis for the first time, especially in the fieldof social sciences.”

—International Statistical Review, 2012

“I have found myself already referring to portions ofthe text as I consider various survey analyses, and Ihave recommended at least portions of it to studentsand colleagues. … an interesting and well-writtenbook … .”

—Ronald D. Fricker, Jr., Journal of Statistical Software,January 2012

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

StatisticalThinking inEpidemiologyYu-Kang Tu and Mark S. GilthorpeUniversity of Leeds, UK

Addressing issues that have plagued researchersthroughout the last decade, this book provides newinsights into the many existing problems in statisticalmodeling and offers several alternative strategies toapproach these problems. Emphasizing the impor-tance of statistical thinking behind all analyses, theauthors use specific examples in epidemiology to illus-trate different model specifications that can imply different sets of causal relationships between variables.Each model is interpreted with regard to the contextof implicit or explicit causal relationships.


New!

BayesianDiseaseMappingHierarchicalModeling in SpatialEpidemiology, Second EditionAndrew B. LawsonMedical University of SouthCarolina, Charleston, USA

This second edition provides an up-to-date, cohesiveaccount of the full range of Bayesian disease mappingmethods and applications. A biostatistics professorand WHO advisor, the author illustrates the use ofBayesian hierarchical modeling in the geographicalanalysis of disease through a range of real-world datasets. This edition includes new chapters on regressionand ecological analysis, putative hazard modeling,and disease map surveillance as well as new appen-dices featuring examples of INLA and CAR models. It also presents expanded material on case eventmodeling and spatiotemporal analysis.

Catalog no. K14543, March 2013, c. 392 pp.ISBN: 978-1-4665-0481-3, $89.95 / £57.99Also available as an eBook

The A-Z ofError-FreeResearchPhillip I. GoodConsultant, Huntington Beach,California, USA

This practical book begins with an overview ofwhen—and when not—to use statistics. It guidesreaders through the planning and data collectionphases and presents various data analysis techniques,including methods for sample size determination. Theauthor then covers techniques for developing modelsthat provide a basis for future research. He also dis-cusses reporting techniques to ensure research effortsget the proper credit. The book concludes with casecontrol and cohort studies. R code is included toimplement the methods.

Catalog no. K14287, August 2012, 269 pp.Soft CoverISBN: 978-1-4398-9737-9, $49.95 / £31.99





11

Clinical Trials


Adaptive DesignMethods inClinical TrialsSecond EditionShein-Chung ChowDuke University School ofMedicine, Durham, NorthCarolina, USA

Mark ChangAMAG Pharmaceuticals, Inc,Lexington, Massachusetts, USA

“This second edition remains a useful referencesource for anyone interested in advancing innova-tive trial designs and wishing to incorporate adap-tations, modifications, and changes to the drugdevelopment process. Five new chapters have beenadded and are all worth reading … . For anyoneworking in and studying clinical research, the bookis worth purchasing and will make a valuable addi-tion to any library. …”

—International Statistical Review, 2012


Coming soon!

RandomizedPhase II CancerClinical TrialsSin-Ho JungDuke University, Durham, North Carolina, USA

There has been a dramatic increase in the use of ran-domized phase II cancer clinical trials in recent yearsbecause of lower sample size requirements when mul-tiple treatments are being evaluated. This accessiblebook covers both the latest developments in method-ology as well as traditional single-arm phase II trialmethods. Keeping the statistical level at a minimum,the book includes many diverse statistical designs andanalysis methods relevant to oncology.

Catalog no. K13295, May 2013, 230 pp.ISBN: 978-1-4398-7185-0, $89.95 / £57.99Also available as an eBook

Adaptive andFlexible ClinicalTrialsRichard ChinInstitute for One World Health,San Francisco, California, USA

By enabling studies to be modified during the courseof the trial, modern adaptive clinical trial designs canmake studies substantially faster, more efficient, andmore powerful than traditional clinical trials. Therecent advances in web-based real-time data entryand novel statistical methods have made adaptiveclinical trials practical and attractive. Suitable for read-ers involved in drug development, this is the first bookthat comprehensively explains all essential aspects ofadaptive clinical trials. Without using highly technicalstatistical jargon, it discusses the design, conduct, andexecution of these trials.


Design andAnalysis ofBridgingStudiesEdited by

Jen-pei Liu, Shein-Chung Chow, and Chin-Fu HsiaoTaking into account the International ConferenceHarmonisation E5 framework for bridging studies, thisbook covers the regulatory requirements, scientificand practical issues, and statistical methodology fordesigning and evaluating bridging studies and multi-regional clinical trials. For bridging studies, theauthors explore ethnic sensitivity, the necessity ofbridging studies, types of bridging studies, and theassessment of similarity between regions based onbridging evidence. For multiregional clinical trials, thetext considers regional differences, assesses the consistency of treatment effect across regions, anddiscusses sample size determination for each region.







Clinical Trials

Clinical Trials in OncologyThird EditionStephanie Green,Jacqueline Benedetti,Angela Smith, and John Crowley

Praise for the Previous Editions:

“With over 60 years combined experience, theauthors are ideally positioned to discuss the variousstatistical issues apparent in clinical trials, identify-ing alternative solutions and providing logical argu-ments for and against the various solutions. Thisbook is also recommended for statisticians activelyinvolved in the design, conduct, and analysis of clin-ical trial data (not only cancer clinical trials).”

—Journal of Biopharmaceutical Statistics

“ALL medical oncology, radiation oncology, surgicaloncology, and clinical research nurse academictraining programs should provide this importanttext to trainees on Day 1.”

—Charles R. Thomas Jr., University of Texas HealthScience Center at San Antonio

This new edition of a bestseller provides a nontechni-cal and thoroughly up-to-date review of methods andissues relevant to clinical trials. The authors emphasizethe importance of proper study design, analysis, anddata management and identify the pitfalls inherent in these processes. The book offers expanded discussions on general clinical trials issues and issuesspecific to Phases I, II, and III. New sections coverinnovations in Phase I designs, randomized Phase IIdesigns, and overcoming the challenges of array data.

• Includes new material on Phase I designs, randomized Phase II designs, array data, andcompeting risks and subset analyses

• Contains further comments on prognostic factor analysis

• Describes complex exploratory analysis methods

Selected Contents:

Introduction. Statistical Concepts. The Design ofClinical Trials. Phase I and Phase I/II Trials. Phase IITrials. Phase III Trials. Data Management and QualityControl. Reporting of Results. Pitfalls. ExploratoryAnalyses. Summary and Conclusions. References.Index.

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

Handbook ofStatistics inClinicalOncologyThird EditionEdited by

John CrowleyCancer Research and Biostatistics,Seattle, Washington, USA

Antje HoeringCancer Research and Biostatistics, University ofWashington, and Fred Hutchinson Cancer ResearchCenter, Seattle, Washington

Addressing the many challenges that have arisen sincethe publication of its predecessor, this third editioncovers the newest developments involved in thedesign and analysis of cancer clinical trials. Accessibleto statisticians in clinical trials as well as oncologistsinterested in clinical trial methodology, the book pres-ents up-to-date statistical approaches for the designand analysis of oncology clinical trials. New topics inthis edition include trial designs for targeted agents,Bayesian trial design, and the inclusion of high-dimen-sional data and imaging techniques.

Selected Contents:

Choosing a Phase I Design. Dose Finding DesignsBased on the Continual Reassessment Method.Pharmacokinetics in Clinical Oncology: StatisticalIssues. Statistics of Phase 0 Trials. CRM Trials forAssessing Toxicity and Efficacy. Seamless Phase I/IITrial Design for Assessing Toxicity and Efficacy forTargeted Agents. Overview of Phase II Clinical Trials.Designs Based on Toxicity and Response. DesignsUsing Time to Event Endpoints/Single Arm versusRandomized Phase II. Phase II Selection Designs.Phase II with Multiple Subgroups: DesignsIncorporating Disease Subtype or GeneticHeterogeneity. Phase II/III Designs. On Use ofCovariates in Randomization and Analysis of ClinicalTrials. Factorial Designs with Time to EventEndpoints. Early Stopping of Clinical Trials.Noninferiority Trials. Phase III Trials for TargetedAgents. Adaptive Trial Designs. Design of a ClinicalTrial for Testing the Ability of a Continuous Marker toPredict Therapy Benefit. Software for Design andAnalysis of Clinical Trials. Cure-Rate Survival Modelsin Clinical Trials. Design and Analysis of Quality ofLife Data. Economic Analyses alongside CancerClinical Trials. Structural and Molecular Imaging inCancer Therapy Clinical Trials. Prognostic FactorStudies. Predictive Modeling of Gene ExpressionData. …

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



13

Clinical Trials


RandomizedClinical Trials of Nonpharma-cologicalTreatmentsEdited by

Isabelle Boutron,Philippe Ravaud, andDavid MoherThis book focuses on the methods of assessing non-pharmacological treatments, highlighting specificissues and discussing trial design. Providing practicalexamples that underline the issues and solutions, thebook is one of the first to exclusively discuss variouscategories of treatments, from surgical procedures topsychotherapy.

• Highlights specific issues in assessing nonpharmacological treatments in trials

• Discusses all possible designs of trials for nonpharmacological treatments

• Provides practical examples to highlight theissues and solutions in assessing nonpharmaco-logical treatments

• Considers various categories of treatments,including implantable devices, rehabilitation,and behavioral interventions

Selected Contents:

Assessing Nonpharmacological Treatments:Theoretical Framework: Blinding inNonpharmacological Randomized Controlled Trials.Placebo in Nonpharmacological Randomized Trials.Complexity of the Intervention. Learning Curves.Clustering Effects in RCTs of NonpharmacologicalInterventions. Assessment of Harm. External Validityand Applicability of Nonpharmacological Trials.Assessing Nonpharmacological Interventions inCluster Randomized Trials. Expertise-Based Trials.Pragmatic Trials and Nonpharmacological Evaluation.Preference Trials. Nonrandomized Studies to Evaluatethe Effects of a Nonpharmacological Intervention. …Assessing Nonpharmacological Treatments:Practical Examples: Assessing CardiothoracicSurgery: Practical Examples. Assessing Obstetrics andGynecology: Practical Examples. Assessing LowerLimb Arthroplasty: Practical Examples. AssessingRadiation Therapy: Practical Examples. AssessingElectroconvulsive Therapy: Practical Examples.Assessing Acupuncture: Practical Examples. AssessingOrthosis: Practical Examples. Assessing Rehabilitation:Practical Examples. …


Design andAnalysis of Non-InferiorityTrialsMark D. Rothmann,Brian L. Wiens, and Ivan S.F. Chan

“It is a pleasure to see a book completely devoted tothe challenging arena of non-inferiority trials. … Iam very impressed with its depth and breadth, andbelieve that it will be an important resource for any-one involved in designing non-inferiority trials. Theauthors weave in many examples, primarily inoncology, as well as a large set of references … amust-have resource for those involved in non-inferi-ority trials for the pharmaceutical industry and amust-read for those new to non-inferiority trials. Aportion of a special topics course in a biostatisticsdepartment could be built around this book … .”

—Erica Brittain, Australian & New Zealand Journal ofStatistics, May 2012

“… recommended for anyone working with clinicaltrials and in particular for those working in latephase drug development. It is an excellent source ofconcepts and statistical methods relevant for biosta-tisticians, clinical epidemiologists, and students. …”

—Steffen Witte, Journal of Biopharmaceutical Statistics, 2012

• Discusses how to assess whether active control iseffective and the size of a likely “random high”bias

• Compares analysis methods with respect to theactive control being selected based on outcomes

• Presents the history of non-inferiority trials

• Describes both Bayesian and frequentist meth-ods as well as asymptotic and exact procedures

Selected Contents:

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

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




Clinical Trials

ControversialStatistical Issuesin Clinical TrialsShein-Chung ChowDuke University School ofMedicine, Durham, North Carolina, USA

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

—David J. Hand, International Statistical Review, 2012

“I would recommend this book since it covers a num-ber of areas that have not been covered in as muchdetail elsewhere. In particular, I thought the chap-ters on molecularly targeted therapies, follow-onbiologics, multiregional clinical trials, and good sta-tistical practices were well written and useful.”

—William Mietlowski, Journal of BiopharmaceuticalStatistics, 2012

• Identifies controversial statistical issues frequentlyencountered in clinical R&D

• Examines critical issues that impact the clinicalinvestigation of a test treatment

• Offers resolutions and recommendations thataddress the problems discussed

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

Selected Contents:

Introduction. Good Statistical Practices. Bench-to-Bedside Translational Research. Bioavailability andBioequivalence. Hypotheses for Clinical Evaluationand Significant Digits. Instability of Sample SizeCalculation. Integrity of Randomization/Blinding.Clinical Strategy for Endpoint Selection. ProtocolAmendments. Seamless Adaptive Trial Designs.Multiplicity in Clinical Trials. Independence of DataMonitoring Committee. Two-Way ANOVA versusOne-Way ANOVA with Repeated Measures.Validation of QOL Instruments. Missing DataImputation. Center Grouping. Non-InferiorityMargin. QT Studies with Recording Replicates.Multiregional Clinical Trials. Dose Escalation Trials. …


Dose Finding bythe ContinualReassessmentMethodYing Kuen CheungColumbia University, New York,New York, USA

This book supplies practical, efficient dose-findingmethods based on statistical research. More than justa cookbook, it provides full, unified coverage of thecontinual reassessment method (CRM) as well as step-by-step guidelines to the automation and parameter-ization of the methods used on a regular basis. The Rpackage dfcrm is used to execute calibration tech-niques and perform simulations before a CRM isimplemented in an actual trial

The author recognizes clinicians’ skepticism of model-based designs and addresses the concerns that thetime, professional, and computational resources necessary for accurate model-based designs can bemajor bottlenecks to the widespread use of appropri-ate dose-finding methods in phase I practice. The theoretically and empirically based methods in thebook should lessen the statistician’s burden andencourage the continuing development and imple-mentation of model-based dose-finding methods.

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

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

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

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

Selected Contents:

Fundamentals. Design Calibration. CRM andBeyond.

Catalog no. C9151, March 2011, 200 pp.ISBN: 978-1-4200-9151-9, $83.95 / £52.99Also available as an eBook



15

Medical Biostatistics and Diagnostics


New!

RegressionModels as aTool in MedicalResearchWerner VachInstitute of Medical Biometry andMedical Informatics, Freiburg,Germany

This text presents the fundamental concepts andimportant aspects of regression models most com-monly used in medical research, including the classi-cal regression model for continuous outcomes, thelogistic regression model for binary outcomes, andthe Cox proportional hazards model for survival data.The author emphasizes adequate use, correct inter-pretation of results, appropriate presentation ofresults, and avoidance of potential pitfalls.

• Helps readers improve their understanding ofthe role of regression models in the medical field

• Illustrates each technique with a concrete example, enabling readers to better appreciatethe properties and theory of the methods

• Uses Stata to demonstrate the practical use ofthe models

• Discusses how and when regression models canfail

• Describes the basic principles behind statisticalcomputations, with more mathematical detailsgiven 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 TECH-NIQUES: Some Useful Technicalities. ComparingRegression Coefficients. Power and Sample Size. TheSelection of the Sample. The Selection of Covariates.Modeling Nonlinear Effects. Transformation ofCovariates. Effect Modification and Interactions.Applying Regression Models to Clustered Data.Applying Regression Models to Longitudinal Data.The Impact of Measurement Error. The Impact ofIncomplete Covariate Data. RISK SCORES AND PRE-DICTORS: Risk Scores. Construction of Predictors.Evaluating the Predictive Performance. Outlook:Construction of Parsimonious Predictors. MISCELLA-NEOUS: Alternatives to Regression Modeling. …MATHEMATICAL DETAILS. Bibliography. Index.

Catalog no. K15111, November 2012, 495 pp.ISBN: 978-1-4665-1748-6, $89.95 / £57.99Also available as an eBook

New!

BayesianMethods inHealthEconomicsGianluca BaioUniversity College London, UK

This book provides an overview of Bayesian methodsfor the analysis of health economic data. After anintroduction to the basic economic concepts andmethods of evaluation, it presents Bayesian statisticsusing accessible mathematics. The next chaptersdescribe the theory and practice of cost-effectivenessanalysis from a statistical viewpoint and Bayesian com-putation, notably MCMC. The final chapter presentsthree detailed case studies covering cost-effectivenessanalyses using individual data from clinical trials, evi-dence synthesis, hierarchical models, and Markovmodels. The text uses WinBUGS and JAGS, with datasets and code available online.

• Provides an overview of Bayesian methods forcost-effectiveness analysis

• Includes all the necessary background on economics and Bayesian statistics

• Presents case studies of the cost-effectivenessanalysis of health care interventions

• Contains several worked examples that guidereaders through the process of health economic evaluation

• Covers the practice of making Bayesian analysis-integrating software, such as JAGS andR, specifically for the application of health economic analysis

• Describes the methodological issues related tothe application of Bayesian inference and decision process in health economics

• Offers code to replicate the examples and anassociated R package to produce systematichealth economic evaluations of Bayesian models on the author’s website

Selected Contents:

Introduction to Health Economic Evaluation.Introduction to Bayesian Inference. Statistical Cost-Effectiveness Analysis. Bayesian Analysis in Practice.Health Economic Evaluation in Practice.






MedicalBiostatisticsThird EditionAbhaya IndrayanThe third edition of this acclaimed reference showshow biostatistics is a useful tool to manage manytypes of medical uncertainties. The author presentsstep-by-step explanations of statistical methods, alongwith numerous real-world examples and worked exercises. Guide charts at the beginning of the bookenable quick access to the relevant statistical proce-dure.

New to the Third Edition• New topics encompassing clinical trials with

stopping rules, adaptive designs, sample size re-estimation and noninferiority margin, dietaryindices, health inequality and ordinal associationmeasures, Poisson distribution, various tests,meta-analysis, ridges and splines, path analysis,clinical agreement assessment, Six Sigma inhealth care, and much more

• More detailed and expanded coverage of survival analysis, ROC curves, equivalence assess-ment, repeated measures ANOVA, and areaunder the concentration curve

• Software illustrations of ANCOVA, repeatedmeasures ANOVA, stepwise regression, quadraticregression, ROC curve, and survival analysis

Selected Contents:

Medical Uncertainties. Basics of Medical Studies.Sampling Methods. Designs of ObservationalStudies. Medical Experiments. Clinical Trials.Numerical Methods for Representing Variation.Presentation of Variation by Figures. SomeQuantitative Aspects of Medicine. Clinimetrics andEvidence-Based Medicine. Measurement ofCommunity Health. Confidence Intervals, Principlesof Tests of Significance, and Sample Size. Inferencefrom Proportions. Relative Risk and Odds Ratio.Inference from Means. Relationships: QuantitativeData. Relationships: Qualitative Dependent. SurvivalAnalysis. Simultaneous Consideration of SeveralVariables. Quality Considerations. Statistical Fallacies.


Statistics ofMedicalImagingTianhu LeiStatistical investigation into technology not only pro-vides a better understanding of the intrinsic featuresof the technology (analysis), but also leads to animproved design of the technology (synthesis). Thisbook gives a theoretical framework for statistical inves-tigation into medical technologies. Rather than offerdetailed descriptions of statistics of basic imaging pro-tocols of x-ray CT and MRI, the book presents amethod to conduct similar statistical investigationsinto more complicated imaging protocols.

• Describes the physical principles and mathematical procedures of medical imagingtechniques

• Presents statistical properties of imaging data(measurements) at each stage in the imagingprocesses 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 andMR image in terms of their statistics and twomodel-based statistical image analysis methods

• Evaluates statistical image analysis methods interms of their detection, estimation, and classification performances

• Indicates that x-ray CT, MRI, PET, and SPECTbelong to a category of imaging: the non-diffraction CT

Selected Contents:

Introduction. X-Ray CT Physics and Mathematics.MRI Physics and Mathematics. Non-DiffractionComputed Tomography. Statistics of X-Ray CTImaging. Statistics of X-Ray CT Image. Statistics ofMR Imaging. Statistics of MR Image. StochasticImage Models. Statistical Image Analysis – I.Statistical Image Analysis – II. Performance Evaluationof Image Analysis Methods.




17



AdvancedBayesianMethods forMedical TestAccuracyLyle D. BroemelingBroemeling and Associates,Medical Lake, Washington, USA

After a review of the usual measures, including speci-ficity, sensitivity, positive predictive value, negativepredictive value, and the area under the ROC curve,this book expands its scope to cover the moreadvanced topics of verification bias, diagnostic testswith imperfect gold standards, and medical testswhere no gold standard is available. The author offersa practical treatment by including R and WinBUGScode in the examples and by employing the Bayesianapproach throughout the text. He also provides practical problems at the end of each chapter.

• Introduces advanced Bayesian methods forassessing the accuracy of medical test results

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

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

• Emphasizes the methods for verification bias and methods for measuring accuracy withoutgold standards

• Provides many real-life examples from theauthor’s wide experience of working in a largemedical center

Selected Contents:

Introduction. Medical Tests and PreliminaryInformation. Preview of the Book. Fundamentals ofDiagnostic Accuracy. Regression and Medical TestAccuracy. Agreement and Test Accuracy. EstimatingTest Accuracy with an Imperfect Reference Standard.Verification Bias and Test Accuracy. Test Accuracy andMedical Practice. Accuracy of Combined Tests.Bayesian Methods for Meta-Analysis. Appendix:Introduction to WinBUGS.


StatisticalEvaluation ofDiagnosticPerformanceTopics in ROCAnalysisKelly H. Zou, Aiyi Liu,Andriy I. Bandos, Lucila Ohno-Machado,and Howard E. Rockette

“This new book by Zou et al significantly contributesto the existing publications by providing shortdescriptions on basic issues and in-depth presenta-tions on a few advanced, research-related issues. … the interested researcher can get inspired readingthis book and discover new, unexplored researchpaths. Another pro of the book, useful for the inter-ested researcher, is the extensive reference list at theend of each chapter. … a valuable starting point forthose conducting basic research on ROC analysisand for applied researchers who are intrigued by theuse of neat methodologies in applications.”

—ISCB News, June 2012

• Presents methods for the statistical validations ofdiagnostic accuracy using ROC analysis

• Includes methods for estimating and comparingdiagnostic test characteristics

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

• Describes Bayesian hierarchical models for estimating diagnostic accuracy

• Discusses clustered and multireader and multimodality ROC and FROC analyses

• Explores biomarkers, sequential designs, andbioinformatics

Selected Contents:

Background and Introduction. Methods forUnivariate and Multivariate Data: Diagnostic RatingScales. Monotone Transformation Models.Combination and Pooling of Biomarkers. BayesianROC Methods. Advanced Approaches andApplications: Sequential Designs of ROCExperiments. Multireader ROC Analysis. Free-Response ROC Analysis. Machine Learning andPredictive Modeling. Discussions and Extensions:Summary and Challenges. Section AppendicesSymbols, Notations and Assumptions. Appendices.Index.





Computational Biostatistics

Coming soon!

Handbook ofSAS® DATA StepProgrammingArthur LiPreparing a data set for analysis is perhaps the mostimportant part of the entire data analysis process.Although there are many books on SAS program-ming, they often completely overlook, or have verybrief coverage of, data manipulation and cleaning.

Designed for novice users, this book gives a broadintroduction to data step programming in SAS®. Itfocuses on understanding how the processes work,with detailed descriptions of the syntax. The textincludes plenty of examples that illustrate the meth-ods and give readers a better understanding of howto prepare their data for analysis in SAS. The book alsoprovides exercises and offers code, data, solutions,and PowerPoint slides on www.crcpress.com.

• Offers an accessible introduction to SAS datastep programming

• Presents lots of real examples to illustrate thetopics

• Includes exercises to enable use as a course text

• Provides code and other material available fordownload on the book’s CRC Press web page

Selected Contents:

Introduction to SAS. Creating Variables Conditionally.The Essence of DATA Step Programming—Understanding How the PDV Works. The BY-GroupProcessing in the DATA Step. Writing Loops in theDATA Step. Array Processing. Combining Datasets.Data Input and Output. DATA Step Functions andCALL Routines. Useful SAS Procedures.

Catalog no. K15213, April 2013, c. 280 pp.ISBN: 978-1-4665-5238-8, $59.95 / £38.99Also available as an eBook

New!

Applied MedicalStatistics UsingSASGeoff DerUniversity of Glasgow, Scotland

Brian S. EverittProfessor Emeritus, King’s CollegeLondon, UK

Designed for medical statisticians, this intermediate-level reference explores the use of SAS for analyzingmedical data. A chapter on visualizing data offers adetailed account of graphics for investigating dataand smoothing techniques. The book also coversmeasurement in medicine, epidemiology/observa-tional studies, meta-analysis, Bayesian methods, andhandling missing data. The book maintains an exam-ple-based approach, with SAS code and outputincluded 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-by-step practical advice

• Focuses on methods most commonly encoun-tered in the analysis of medical data, includingregression, longitudinal and survival data analysis, ANOVA and ANCOVA, and GAMs

Selected Contents:

An Introduction to SAS. Statistics and Measurementin Medicine. Clinical Trials. Epidemiology. Meta-analysis. Analysis of Variance and Covariance. ScatterPlots, Correlation, Simple Regression, andSmoothing. Multiple Linear Regression. LogisticRegression. The Generalized Linear Model.Generalized Additive Models. The Analysis ofLongitudinal Data I. The Analysis of LongitudinalData II: Linear Mixed-Effects Models for NormalResponse Variables. The Analysis of Longitudinal DataIII: Non-Normal Responses. Survival Analysis. Cox’sProportional Hazards Models for Survival Data.Bayesian Methods. Missing Values.

Catalog no. K13087, October 2012, 559 pp.ISBN: 978-1-4398-6797-6, $89.95 / £57.99Also available as an eBook



19



The R PrimerClaus Thorn EkstrømUniversity of Copenhagen,Frederiksberg, Denmark

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

—Claire Keeble, Journal of Applied Statistics, 2012

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

This primer provides a collection of concise examplesand solutions to R problems frequently encounteredby new users of this statistical software. Numerousexamples illustrate a specific situation, topic, or prob-lem, including data importing, data management,classical statistical analyses, and high-quality graphicsproduction. Each example is self contained andincludes 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 thebook’s supporting website.

• Presents concise examples and solutions to common problems in R

• Explains how to read and interpret output fromstatistical 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:

Importing Data. Manipulating Data. StatisticalAnalyses. Graphics. R. Bibliography. Index.

Catalog no. K12876, August 2011, 299 pp.Soft CoverISBN: 978-1-4398-6206-3, $39.95 / £26.99Also available as an eBook

PracticalStatisticalMethodsA SAS ProgrammingApproachLakshmi PadgettCentocor, Malvern, Pennsylvania,USA

This book presents a broad spectrum of statisticalmethods useful for researchers without an extensivestatistical background. Omitting mathematical detailsand complicated formulae, it provides SAS programsto carry out the necessary analyses and draw appro-priate inferences for common statistical problems.

The author describes methods used for quantitativedata and continuous data following normal and non-normal distributions. She also focuses on simple linearregression, logistic regression, and the proportionalhazards model. The final chapter briefly discusses suchmiscellaneous topics as propensity scores, misclassifi-cation 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 diagnosticerrors, jackknife estimators, bootstrap method,microarrays, group testing, multidimensionalscaling, choice-based conjoint analysis, andmeta-analysis

Selected Contents:

Introduction. Qualitative Data. Continuous NormalData. Nonparametric Methods. Regression.Miscellaneous Topics. References and SelectedBibliography. Index.






BayesianAnalysis MadeSimpleAn Excel GUI forWinBUGSPhil WoodwardPfizer, LTD, Sandwich, Kent, UK

“The author in writing this text has succeeded inmaking Bayesian analysis relatively simple througha graphical user interface (GUI) for WinBUGS—BugsXLA, which resides within Excel. … I recommendthe book to anyone contemplating the use ofBayesian methods for the first time and alreadyfamiliar with Excel … . The text provides an idealintroduction to Bayesian approaches using Exceland ultimately will encourage the reader to migrateto WinBUGS proper.”

—International Statistical Review, 80, 2012

“… will help a competent statistician to run aBayesian analysis of a generalized linear mixedmodel almost effortlessly.”

—John Paul Gosling, Journal of Applied Statistics, 2012

• Shows how to integrate the power of Bayesianmethods with the convenience that comes withusing Excel to store and explore data

• Provides numerous case studies that cover a vast range of model types and illustrate how touse BugsXLA to undertake an appropriateBayesian analysis

• Explains how even some of the more complexaspects of model specification can be routinelyapplied

• 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 ReferenceManual. Normal Linear Models (NLMs). GeneralizedLinear Models. Normal Linear Mixed Models.Generalized Linear Mixed Models. Emax or Four-Parameter Logistic Non-Linear Models. BayesianVariable Selection. Longitudinal and RepeatedMeasures Models. Robust Models. Beyond BugsXLA:Extending the WinBUGS Code. Appendices.


New!

The BUGS BookA PracticalIntroduction toBayesian AnalysisDavid Lunn, Chris Jackson, Nicky Best, Andrew Thomas, andDavid Spiegelhalter

“The most anticipated applied Bayesian text of thelast 20 years, The BUGS Book is like a wonderfulalbum by an established rock supergroup … theauthors have created a masterpiece well worth thewait. The book offers the perfect mix of basic proba-bility calculus, Bayes and MCMC basics, an incredi-bly broad array of useful statistical models, and aBUGS tutorial and user manual complete with all the‘tricks’ one would expect from the team that invent-ed the language. … provid[es] accessible yet com-prehensive instruction in its proper use. A must-ownfor any working applied statistical modeler.”

—Bradley P. Carlin, University of Minnesota

Authored by the team that developed the BUGS soft-ware, this text presents complete coverage of all thefunctionalities of BUGS, including prediction, missingdata, model criticism, and prior sensitivity. It featuresa large number of worked examples, a wide range ofapplications from various disciplines, and numerousdetailed exercises in every chapter.

• Provides an accessible introduction to Bayesiananalysis 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. MonteCarlo Simulations using BUGS. Introduction toBayesian Inference. Introduction to Markov ChainMonte Carlo Methods. Prior Distributions. RegressionModels. Categorical Data. Model Checking andComparison. Issues in Modeling. HierarchicalModels. Specialized Models. DifferentImplementations of BUGS. Appendices. Bibliography.Index.

Catalog no. C8490, October 2012, 399 pp.Soft CoverISBN: 978-1-58488-849-9, $49.95 / £24.99Also available as an eBook



21

Survival Analysis


MultivariateSurvivalAnalysis andCompetingRisksMartin J. CrowderImperial College, University ofLondon, UK

Suitable for graduate students and researchers in statistics and biostatistics as well as those in the med-ical field, epidemiology, and social sciences, this bookintroduces univariate survival analysis and extends itto the multivariate case. It also covers competing risksand counting processes and provides many real-worldexamples, exercises, and R code. The text discussessurvival data, survival distributions, frailty models,parametric methods, multivariate data and distribu-tions, copulas, continuous failure, parametric likeli-hood inference, and non- and semi-parametric methods.

• Provides a broad overview of multivariate survival analysis, competing risks, and countingprocesses

• Contains many real-world examples to illustratethe methodology

• Presents a clear style appropriate for graduatestudents in statistics

• Offers a supporting R package for the analyses,with some code in the book

Selected Contents:

Univariate Survival Analysis: Survival Data. SurvivalDistributions. Frailty Models. Parametric Methods.Discrete Time: Non- and Semi-Parametric Methods.Continuous-Time: Non- and Semi-ParametricMethods. Multivariate Survival Analysis:Multivariate Data and Distributions. Frailty andCopulas. Repeated Measure. Wear and Degradation.Competing Risks: Continuous Failure Times andTheir Causes. Parametric Likelihood Inference. LatentFailure Times: Probability Distributions. DiscreteFailure Times in Competing Risks. Hazard-BasedMethods for Continuous Failure Times. Latent FailureTimes: Identifiability Crises. Counting Processes inSurvival Analysis: Some Basic Concepts. SurvivalAnalysis. Non- and Semi-Parametric Methods.


Coming soon!

SurvivalAnalysis inMedicine andGeneticsJialiang LiNational University of Singapore

Shuangge MaYale University, New Haven,Connecticut, USA

This text introduces up-to-date statistical methods forsurvival data analysis in medicine and genetics. Alongwith classical results, it presents new developments ininterval censoring, statistical diagnostics with time-dependent outcomes, analysis of ultra high-dimen-sional data sets, cure rate models, and repeated measure data. Suitable for both graduate studentsand biomedical researchers, the text covers applica-tions in cancer studies, medical diagnosis, genetics,and genomics. It provides R code and example datasets online.

Catalog no. K14175, June 2013, c. 384 pp.ISBN: 978-1-4398-9311-1, $99.95 / £63.99Also available as an eBook

DynamicPrediction inClinical SurvivalAnalysisHans van Houwelingenand Hein PutterLeiden University, The Netherlands

In the last 20 years, dynamic prediction models havebeen extensively used to monitor patient prognosis insurvival analysis. Written by one of the pioneers in thearea, this book synthesizes these developments in aunified framework. It covers a range of models,including prognostic and dynamic prediction of sur-vival using genomic data and time-dependent infor-mation. The text includes numerous examples usingreal data taken from the authors’ collaborativeresearch. R programs are provided for implementingthe methods.






Statistical Genetics and Bioinformatics

Statistics andData Analysisfor MicroarraysUsing R andBioconductorSecond Edition Sorin DrăghiciWayne State University, Detroit,Michigan, USA

This richly illustrated, bestselling text provides a clearand rigorous description of powerful analysis tech-niques and algorithms for mining and interpretingbiological information. It takes a hands-on, example-based approach that explains the basics of R andmicroarray technology as well as how to choose andapply the proper data analysis tool to specific prob-lems. Now using R and Bioconductor, this updatedand greatly expanded edition includes 14 new chapters and offers the R code on a CD-ROM.

• Presents an in-depth treatment of the statisticaland data analysis aspects used in microarraysand bioinformatics

• Provides the option of learning R in parallel withlearning about data analysis

• Covers background material for those with alimited mathematical, genetic, or molecular biology foundation

Selected Contents:

The Cell and Its Basic Mechanisms. Microarrays.Reliability and Reproducibility Issues in DNAMicroarray Measurements. Image Processing.Introduction to R. Bioconductor: Principles andIllustrations. Elements of Statistics. ProbabilityDistributions. Basic Statistics in R. StatisticalHypothesis Testing. Classical Approaches to DataAnalysis. Analysis of Variance (ANOVA). LinearModels in R. Experiment Design. MultipleComparisons. Analysis and Visualization Tools.Cluster Analysis. Quality Control. Data Pre-Processingand Normalization. Methods for SelectingDifferentially Regulated Genes. The Gene Ontology(GO). Functional Analysis and BiologicalInterpretation of Microarray Data. Uses, Misuses, andAbuses in GO Profiling. A Comparison of SeveralTools for Ontological Analysis. Focused Microarrays—Comparison and Selection. ID Mapping Issues.Pathway Analysis. Machine Learning Techniques. TheRoad Ahead. References.


Coming soon!

Introduction toBiologicalNetworksAlpan RavalClaremont Graduate University,California, USA

Animesh Ray

“Raval and Ray provide a comprehensive and modern exposition of a rapidly evolving field: network biology. This text will help biology, mathe-matics, and computer science students alike tobecome acquainted with the history and frontiers ofresearch in this exciting area.”

—Joshua B. Plotkin, University of Pennsylvania

“Finally a book has arrived that describes the basicsof biological complexity. Written by leading scien-tists Raval and Ray, it provides a scholarly accountof the concepts of network theory. It describes ingreat detail the experimental and computationalmethods for identifying and predicting biologicalnetworks and reveals how network analysis can beapplied to solve fundamental questions in biologyand medicine. Introduction to Biological Networksis easily the best read available on this importantand rapidly developing field.”

—Cornelis Murre, University of California-San Diego

This book discusses the general principles behind network models and the essential concepts in themathematical modeling of molecular regulatory net-works in biology. It addresses computational methodsfor deriving network models from data. It alsoexplores the testing of inferred networks by perturba-tion analysis on real biological systems using genom-ic techniques.

• Highlights current progress in functionalgenomics 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 andgraph theory to questions in biology

Selected Contents:

Introduction. Inferring Networks from Data. TestingInferred Networks. Small Model Networks. TractableNetwork Models. Discussion and Synthesis.

Catalog no. C4630, April 2013, c. 328 pp.ISBN: 978-1-58488-463-7, $79.95 / £49.99Also available as an eBook



Exercises and Solutions in Statistical Theory• Provides a range of exercises to supple-

ment a course in statistical theory

• Covers correlated data analysis, latentclass analysis, Bayesian analysis, measure-ment error, and multilevel modeling

• Presents applications in medicine, epidemiology, actuarial science, social sciences, engineering, and genetics

• Includes a solutions manual upon qualify-ing course adoption

Catalog no. K16626, May 2013, c. 390 pp.Soft Cover, ISBN: 978-1-4665-7289-8$59.95 / £38.99Also available as an eBook

Survival Analysis in Medicine andGenetics• Shows students how to use various statistical

methods for analyzing survival data

• Provides a detailed introduction to newly developed methods for ultra high-dimensional data in genetics and genomics

• Offers data sets and computer programs online

• Includes a solutions manual upon qualifyingcourse adoption

Catalog no. K14175, June 2013, c. 384 pp.ISBN: 978-1-4398-9311-1$99.95 / £63.99Also available as an eBook




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Biostatistics