Handbook of Statistics in Clinical Oncology / Edition 2

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Overview

A compendium of cutting-edge statistical approaches to solving problems in clinical oncology, Handbook of Statistics in Clinical Oncology, Second Edition focuses on clinical trials in phases I, II, and III, proteomic and genomic studies, complementary outcomes and exploratory methods. Cancer Forum called the first edition a ¼good reference book for statisticians who will be designing and analyzing cancer trials." The second edition includes over 1000 references, more than forty world-renowned contributors, and 300 equations, tables, and drawings.

During the five years since publication of the first edition, there has been an explosion in the technological capabilities supporting genomic and proteomic research, which are is now firmly implanted in clinical oncology. Reflecting these developments, the second edition contains a new section devoted to analyses of high-throughput data and bioinformatics. Previous chapters of the first edition have been revised to reflect current state of the art in their respective domains. The intended audience is primarily statisticians working in cancer and more generally, in any discipline of medicine. But oncologists too will find the material accessible and will benefit from a rudimentary understanding of the fundamental concepts laid forth in each chapter.

Completely revised while keeping the features that made the first edition a bestseller, this is the best single source for up-to-date statistical approaches to research in clinical medicine. More than just an update of the handbook that became the gold standard, this second edition brings you fully into the genomic era of medicine.

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Editorial Reviews

International Statistic Institute
...a compendium of statistical approaches to the problems facing those trying to make progress against cancer....the time is propitious for this book.
Booknews
A compendium of statistical approaches to the problems facing researchers trying to make progress against cancer. The focus is on cancer clinical trials, but several of the contributions also apply to observational studies, and many chapters generalize beyond cancer research. The intended audience is primarily statisticians working in cancer research, but oncologists may also find it helpful. The work's sections cover phase 1, 2, and 3 trials; complementary outcomes; prognostic factors and exploratory analysis; and interpreting clinical trials. Annotation c. Book News, Inc., Portland, OR (booknews.com)
Doody's Review Service
Reviewer: Gerald R Hobbs, PhD (West Virginia University)
Description: This survey of the statistical methods that are commonly used in cancer research is a collection of essays by numerous authorities.The emphasis is on clinical trials.
Purpose: The stated purpose is to provide an update on statistical methods that are currently used in cancer trials. I am not aware of another book that pulls this information together in one place. The book seems to cover the topics very thouroughly.
Audience: The target audience is "statisticians working in cancer research," which clearly is the group that would most likely benefit from from this book. Certain very quantitative people in other disciplines might also benefit from it. There are many authors here and, generally, they are world class authorities.
Features: This book surveys the state of the art as it applies to statistical methods and cancer research. It is initially organized in phase I, phase II, phase III order, which is natural enough. The subsequent chapters could probably be in any order. The material on phase I trials is impressive, given that that topic is often covered in a cursory way. The index appears to be an afterthought and isn't very useful.
Assessment: There are many authors here and style and quality of writing must be expected to vary. The editor has done a nice job of melding it all together into a desktop reference. This book puts a lot of information into one place for quick reference by those of us who need to plan trials.

3 Stars from Doody
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Product Details

  • ISBN-13: 9780824723392
  • Publisher: CRC Press
  • Publication date: 11/23/2005
  • Edition description: REV
  • Edition number: 2
  • Pages: 640
  • Product dimensions: 6.40 (w) x 9.30 (h) x 1.30 (d)

Table of Contents

Contents
Phase I Trials
Overview of Phase I Trials, L. Edler and I. Burkholder
Phase I and Phase I/II Dose Finding Algorithms Using Continual Reassessment Method, J. O’Quigley
Choosing a Phase I Design, B. E. Storer
Pharmacokinetics in Clinical Oncology: Statistical Issues, G.L. Rosner, P. Müller, S. Lunagomez, and P.A. Thompson
Practical Implementation of the Continual Reassessment Method, N. Ishizuka and S. Morita
Phase II Trials
Overview of Phase II Clinical Trials, S. Green
Designs Based on Toxicity and Response, G.R. Petroni and M.R. Conaway
Phase II Trials Using Time-to-Event Endpoints, C.M. Tangen and J.J. Crowley
Phase II Selection Designs, P.Y. Liu, J. Moon, and M. LeBlanc
Bayesian Sensitivity Analyses of Confounded Treatment Effects, P.F. Thall and X. Wang
Phase III Trials
On Use of Covariates in Randomization and Analysis of Clinical Trials, G.L. Anderson, M. LeBlanc, P.Y. Liu, and J. Crowley
Factorial Designs with Time to Event Endpoints, S. Green
Noninferiority Trials, K.J. Kopecky and S. Green
Power and Sample Size for Phase III Clinical Trials of Survival, J.J. Shuster
Early Stopping of Cancer Clinical Trials, J.J. Dignam, J. Bryant, and H.S. Wieand
Design and Analysis of Quality of Life Data, A.B. Troxel, and C.M. Moinpour
Economic Analyses Alongside Cancer Clinical Trials, S.D. Ramsey
Exploratory Analysis and Prognostic Factors
Prognostic Factor Studies, M. Schumacher, N. Holländer, G. Schwarzer, and W. Sauerbrei
Statistical Methods to Identify Predictive Factors, K. Ulm, M. Seebauer, S. Eberle, M. Reck, and S. Hessler
Explained Variation in Propotional Hazards Regression, J.O. Quigly and R. Xu
Constructing Prognostic Groups by Tree-Based Partitioning and Peeling Methods, M. LeBlanc, E. Rasmussen, and J. Crowley
Clinical Monitoring Based on Joint Models for Longitudinal Biomarkers and Event Times, D. Pauler Ankerst and D.M. Finkelstein
High-Throughput DatA and Bioinformatics
Some Practical Considerations for Analysis of Spotted Microarray Data, L. Hsu, J.R. Faulkner, D. Grove, and D. Pauler Ankerst
Statistical Applications Using DNA Microarrays for Cancer Diagnosis and Prognosis, S. Matsui
Profiling High-Dimensional Protein Expression Using MALDI-TOF: Mass Spectrometry for Biomarker Discovery, Y. Yasui, T. Randolph, and Z. Feng
Statistical Approaches for High Dimensional Data Derived from High Throughput Assays: A Case Study of Protein Expression Levels in Lung Cancer, Y. Shyr
Spatial Modeling of Multilocus Data, D.V. Conti, D.O. Stram, J. Molitor, P. Marjoram, and D.C. Thomas
Software for Genomic Data, R. Gentleman
Interpreting Clinical Trials
Interpreting Longitudinal Studies of QOL with Nonignorable Dropout, D.L. Fairclough
Why Kaplan-Meier Fails and Cumulative Incidence Succeeds when Estimating Failure Probabilities in the Presence of Competing Risks, T.A. Gooley, W. Leisenring, J. Crowley, and B.E. Storer
Pitfalls in the Design, Conduct and Analysis of Randomized Clinical Trials, R.J. Stephens
Dose-Intensity Analysis, J. Pater
Sequential Randomization, J. Pater and J. Crowley

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