In addition to our prestigious portfolio of statistics journals, Wiley also publishes books in all fields of statistics. Recently published books include:
Modern Industrial Statistics is a leading reference and guide to the statistics tools widely used in industry and services by Ron S. Kenett and Shelemyahu Zacks. Designed to help professionals and students easily access relevant theoretical and practical information in a single volume, this standard resource employs a computer-intensive approach to industrial statistics and provides numerous examples and procedures in the popular R language and for MINITAB and JMP statistical analysis software. Divided into two parts, the text covers the principles of statistical thinking and analysis, bootstrapping, predictive analytics, Bayesian inference, time series analysis, acceptance sampling, statistical process control, design and analysis of experiments, simulation and computer experiments, and reliability and survival analysis. Part A, on computer age statistical analysis, can be used in general courses on analytics and statistics. Part B is focused on industrial statistics applications.
The fully revised third edition covers the latest techniques in R, MINITAB and JMP, and features brand-new coverage of time series analysis, predictive analytics and Bayesian inference. New and expanded simulation activities, examples, and case studies—drawn from the electronics, metal work, pharmaceutical, and financial industries—are complemented by additional computer and modeling methods. Helping readers develop skills for modeling data and designing experiments, this comprehensive volume:
- Explains the use of computer-based methods such as bootstrapping and data visualization
- Covers nonstandard techniques and applications of industrial statistical process control (SPC) charts
- Contains numerous problems, exercises, and data sets representing real-life case studies of statistical work in various business and industry settings
- Includes access to a companion website that contains an introduction to R, sample R code, csv files of all data sets, JMP add-ins, and downloadable appendices
- Provides an author-created R package, mistat, that includes all data sets and statistical analysis applications used in the book
Part of the acclaimed Statistics in Practice series, Modern Industrial Statistics with Applications in R, MINITAB, and JMP, Third Edition, is the perfect textbook for advanced undergraduate and postgraduate courses in the areas of industrial statistics, quality and reliability engineering, and an important reference for industrial statisticians, researchers, and practitioners in related fields. The mistat R-package is available from the R CRAN repository.
Edited by Richard D. Riley, Jayne F. Tierney, and Lesley A. Stewart, Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research provides a comprehensive introduction to the fundamental principles and methods that healthcare researchers need when considering, conducting or using individual participant data (IPD) meta-analysis projects. Written and edited by researchers with substantial experience in the field, the book details key concepts and practical guidance for each stage of an IPD meta-analysis project, alongside illustrated examples and summary learning points.
Split into five parts, the book chapters take the reader through the journey from initiating and planning IPD projects to obtaining, checking, and meta-analysing IPD, and appraising and reporting findings. The book initially focuses on the synthesis of IPD from randomised trials to evaluate treatment effects, including the evaluation of participant-level effect modifiers (treatment-covariate interactions). Detailed extension is then made to specialist topics such as diagnostic test accuracy, prognostic factors, risk prediction models, and advanced statistical topics such as multivariate and network meta-analysis, power calculations, and missing data.
Intended for a broad audience, the book will enable the reader to:
- Understand the advantages of the IPD approach and decide when it is needed over a conventional systematic review
- Recognise the scope, resources and challenges of IPD meta-analysis projects
- Appreciate the importance of a multi-disciplinary project team and close collaboration with the original study investigators
- Understand how to obtain, check, manage and harmonise IPD from multiple studies
- Examine risk of bias (quality) of IPD and minimise potential biases throughout the project
- Understand fundamental statistical methods for IPD meta-analysis, including two-stage and one-stage approaches (and their differences), and statistical software to implement them
- Clearly report and disseminate IPD meta-analyses to inform policy, practice and future research
- Critically appraise existing IPD meta-analysis projects
- Address specialist topics such as effect modification, multiple correlated outcomes, multiple treatment comparisons, non-linear relationships, test accuracy at multiple thresholds, multiple imputation, and developing and validating clinical prediction models
In September 2021, Evaluating Synergy: Statistical Design and Analysis of Drug Combination Studies will be published. By Ming Tan, Hongbin Fang, and Douglas Ross, and containing the historical and statistical information necessary to choose an analysis method and successful drug combination, Evaluating Synergy provides a systematic introduction of statistical methods for optimally designing and analyzing combination studies in cancer, anti-viral, and other therapeutic areas. This practical guide, also part of the Statistics in Practice series, provides scientists in translational research, data analysts, and statisticians in cancer research with a detailed discussion on the challenging case of three or multi-drug combinations. Numerous examples accompany a presentation that illustrates experimental design considerations for modern drug analysis.