Statistics in Practice
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Modeling Online Auctions
by Wolfgang Jank
Part 91 of the Statistics in Practice series
Explore cutting-edge statistical methodologies for collecting, analyzing, and modeling online auction data
Online auctions are an increasingly important marketplace, as the new mechanisms and formats underlying these auctions have enabled the capturing and recording of large amounts of bidding data that are used to make important business decisions. As a result, new statistical ideas and innovation are needed to understand bidders, sellers, and prices. Combining methodologies from the fields of statistics, data mining, information systems, and economics, Modeling Online Auctions introduces a new approach to identifying obstacles and asking new questions using online auction data.
The authors draw upon their extensive experience to introduce the latest methods for extracting new knowledge from online auction data. Rather than approach the topic from the traditional game-theoretic perspective, the book treats the online auction mechanism as a data generator, outlining methods to collect, explore, model, and forecast data. Topics covered include:
• Data collection methods for online auctions and related issues that arise in drawing data samples from a Web site
• Models for bidder and bid arrivals, treating the different approaches for exploring bidder-seller networks
• Data exploration, such as integration of time series and cross-sectional information, curve clustering, semi-continuous data structures, and data hierarchies
• The use of functional regression as well as functional differential equation models, spatial models, and stochastic models for capturing relationships in auction data
• Specialized methods and models for forecasting auction prices and their applications in automated bidding decision rule systems
Throughout the book, R and MATLAB software are used for illustrating the discussed techniques. In addition, a related Web site features many of the book's datasets and R and MATLAB code that allow readers to replicate the analyses and learn new methods to apply to their own research.
Modeling Online Auctions is a valuable book for graduate-level courses on data mining and applied regression analysis. It is also a one-of-a-kind reference for researchers in the fields of statistics, information systems, business, and marketing who work with electronic data and are looking for new approaches for understanding online auctions and processes.
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Binary Data Analysis of Randomized Clinical Trials With Noncompliance
by Kung-Jong Lui
Part 99 of the Statistics in Practice series
It is quite common in a randomized clinical trial (RCT) to encounter patients who do not comply with their assigned treatment. Since noncompliance often occurs non-randomly, the commonly-used approaches, including both the as-treated (AT) and as-protocol (AP) analysis, and the intent-to-treat (ITT) (or as-randomized) analysis, are all well known to possibly produce a biased inference of the treatment efficacy.
This book provides a systematic and organized approach to analyzing data for RCTs with noncompliance under the most frequently-encountered situations. These include parallel sampling, stratified sampling, cluster sampling, parallel sampling with subsequent missing outcomes, and a series of dependent Bernoulli sampling for repeated measurements. The author provides a comprehensive approach by using contingency tables to illustrate the latent probability structure of observed data. Using real-life examples, computer-simulated data and exercises in each chapter, the book illustrates the underlying theory in an accessible, and easy to understand way.
Key features:
• Consort-flow diagrams and numerical examples are used to illustrate the bias of commonly used approaches, such as, AT analysis, AP analysis and ITT analysis for a RCT with noncompliance.
• Real-life examples are used throughout the book to explain the practical usefulness of test procedures and estimators.
• Each chapter is self-contained, allowing the book to be used as a reference source.
• Includes SAS programs which can be easily modified in calculating the required sample size.
Biostatisticians, clinicians, researchers and data analysts working in pharmaceutical industries will benefit from this book. This text can also be used as supplemental material for a course focusing on clinical statistics or experimental trials in epidemiology, psychology and sociology.
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Understanding Biostatistics
by Anders Källén
Part 100 of the Statistics in Practice series
“Understanding Biostatistics” looks at the fundamentals of biostatistics, using elementary statistics to explore the nature of statistical tests.
This book is intended to complement first-year statistics and biostatistics textbooks. The main focus here is on ideas, rather than on methodological details. Basic concepts are illustrated with representations from history, followed by technical discussions on what different statistical methods really mean. Graphics are used extensively throughout the book in order to introduce mathematical formulae in an accessible way.
Key features:
• Discusses confidence intervals and p-values in terms of confidence functions.
• Explains basic statistical methodology represented in terms of graphics rather than mathematical formulae, whilst highlighting the mathematical basis of biostatistics.
• Looks at problems of estimating parameters in statistical models and looks at the similarities between different models.
• Provides an extensive discussion on the position of statistics within the medical scientific process.
• Discusses distribution functions, including the Guassian distribution and its importance in biostatistics.
This book will be useful for biostatisticians with little mathematical background as well as those who want to understand the connections in biostatistics and mathematical issues.
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Statistical Methods for Trend Detection and Analysis in the Environmental Sciences
by Richard Chandler
Part 105 of the Statistics in Practice series
The need to understand and quantify change is fundamental throughout the environmental sciences. This might involve describing past variation, understanding the mechanisms underlying observed changes, making projections of possible future change, or monitoring the effect of intervening in some environmental system. This book provides an overview of modern statistical techniques that may be relevant in problems of this nature.
Practitioners studying environmental change will be familiar with many classical statistical procedures for the detection and estimation of trends. However, the ever increasing capacity to collect and process vast amounts of environmental information has led to growing awareness that such procedures are limited in the insights that they can deliver. At the same time, significant developments in statistical methodology have often been widely dispersed in the statistical literature and have therefore received limited exposure in the environmental science community. This book aims to provide a thorough but accessible review of these developments. It is split into two parts: the first provides an introduction to this area and the second part presents a collection of case studies illustrating the practical application of modern statistical approaches to the analysis of trends in real studies.
Key Features:
• Presents a thorough introduction to the practical application and methodology of trend analysis in environmental science.
• Explores non-parametric estimation and testing as well as parametric techniques.
• Methods are illustrated using case studies from a variety of environmental application areas.
• Looks at trends in all aspects of a process including mean, percentiles and extremes.
• Supported by an accompanying website featuring datasets and R code.
The book is designed to be accessible to readers with some basic statistical training, but also contains sufficient detail to serve as a reference for practising statisticians. It will therefore be of use to postgraduate students and researchers both in the environmental sciences and in statistics.
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Operational Risk Management
A Practical Approach to Intelligent Data Analysis
by Various Authors
Part 106 of the Statistics in Practice series
Models and methods for operational risks assessment and mitigation are gaining importance in financial institutions, healthcare organizations, industry, businesses and organisations in general. This book introduces modern Operational Risk Management and describes how various data sources of different types, both numeric and semantic sources such as text can be integrated and analyzed. The book also demonstrates how Operational Risk Management is synergetic to other risk management activities such as Financial Risk Management and Safety Management.
Operational Risk Management: a practical approach to intelligent data analysis provides practical and tested methodologies for combining structured and unstructured, semantic-based data, and numeric data, in Operational Risk Management (OpR) data analysis.
Key Features:
• The book is presented in four parts: 1) Introduction to OpR Management, 2) Data for OpR Management, 3) OpR Analytics and 4) OpR Applications and its Integration with other Disciplines.
• Explores integration of semantic, unstructured textual data, in Operational Risk Management.
• Provides novel techniques for combining qualitative and quantitative information to assess risks and design mitigation strategies.
• Presents a comprehensive treatment of "near-misses" data and incidents in Operational Risk Management.
• Looks at case studies in the financial and industrial sector.
• Discusses application of ontology engineering to model knowledge used in Operational Risk Management.
Many real-life examples are presented, mostly based on the MUSING project co-funded by the EU FP6 Information Society Technology Programme. It provides a unique multidisciplinary perspective on the important and evolving topic of Operational Risk Management. The book will be useful to operational risk practitioners, risk managers in banks, hospitals and industry looking for modern approaches to risk management that combine an analysis of structured and unstructured data. The book will also benefit academics interested in research in this field, looking for techniques developed in response to real world problems.
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Comparing Clinical Measurement Methods
A Practical Guide
by Bendix Carstensen
Part 108 of the Statistics in Practice series
This book provides a practical guide to analysis of simple and complex method comparison data, using Stata, SAS and R. It takes the classical Limits of Agreement as a starting point and presents it in a proper statistical framework. The model serves as a reference for reporting sources of variation and for providing conversion equations and plots between methods for practical use, including prediction uncertainty.
• Presents a modeling framework for analysis of data and reporting of results from comparing measurements from different clinical centers and/or different methods.
• Provides the practical tools for analyzing method comparison studies along with guidance on what to report and how to plan comparison studies and advice on appropriate software.
• Illustrated throughout with computer examples in R.
• Supported by a supplementary website hosting an R-package that performs the major part of the analyses needed in the area.
• Examples in SAS and Stata for the most common situations are also provided.
• Written by an acknowledged expert on the subject, with a long standing experience as a biostatistician in a clinical environment and a track record of delivering training on the subject.
Biostatisticians, clinicians, medical researchers and practitioners involved in research and analysis of measurement methods and laboratory investigations will benefit from this book. Students of statistics, biostatistics, and the chemical sciences will also find this book useful.
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Maximum Likelihood Estimation and Inference
With Examples in R, SAS and ADMB
by Russell B. Millar
Part 111 of the Statistics in Practice series
This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood and moves through to the latest developments in maximum likelihood methodology, including general latent variable models and new material for the practical implementation of integrated likelihood using the free ADMB software. Fundamental issues of statistical inference are also examined, with a presentation of some of the philosophical debates underlying the choice of statistical paradigm.
Key features:
• Provides an accessible introduction to pragmatic maximum likelihood modelling.
• Covers more advanced topics, including general forms of latent variable models (including non-linear and non-normal mixed-effects and state-space models) and the use of maximum likelihood variants, such as estimating equations, conditional likelihood, restricted likelihood and integrated likelihood.
• Adopts a practical approach, with a focus on providing the relevant tools required by researchers and practitioners who collect and analyze real data.
• Presents numerous examples and case studies across a wide range of applications including medicine, biology and ecology.
• Features applications from a range of disciplines, with implementation in R, SAS and/or ADMB.
• Provides all program code and software extensions on a supporting website.
• Confines supporting theory to the final chapters to maintain a readable and pragmatic focus of the preceding chapters.
This book is not just an accessible and practical text about maximum likelihood, it is a comprehensive guide to modern maximum likelihood estimation and inference. It will be of interest to readers of all levels, from novice to expert. It will be of great benefit to researchers, and to students of statistics from senior undergraduate to graduate level. For use as a course text, exercises are provided at the end of each chapter.
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Simplicity, Complexity and Modelling
by Various Authors
Part 114 of the Statistics in Practice series
Several points of disagreement exist between different modelling traditions as to whether complex models are always better than simpler models, as to how to combine results from different models and how to propagate model uncertainty into forecasts. This book represents the result of collaboration between scientists from many disciplines to show how these conflicts can be resolved.
Key Features:
• Introduces important concepts in modelling, outlining different traditions in the use of simple and complex modelling in statistics.
• Provides numerous case studies on complex modelling, such as climate change, flood risk and new drug development.
• Concentrates on varying models, including flood risk analysis models, the petrol industry forecasts and summarizes the evolution of water distribution systems.
• Written by experienced statisticians and engineers in order to facilitate communication between modellers in different disciplines.
• Provides a glossary giving terms commonly used in different modelling traditions.
This book provides a much-needed reference guide to approaching statistical modelling. Scientists involved with modelling complex systems in areas such as climate change, flood prediction and prevention, financial market modelling and systems engineering will benefit from this book. It will also be a useful source of modelling case histories.
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Modern Analysis of Customer Surveys
With Applications Using R
by Ron S. Kenett
Part 117 of the Statistics in Practice series
Customer survey studies deals with customers, consumers and user satisfaction from a product or service. In practice, many of the customer surveys conducted by business and industry are analyzed in a very simple way, without using models or statistical methods. Typical reports include descriptive statistics and basic graphical displays. As demonstrated in this book, integrating such basic analysis with more advanced tools, provides insights on non-obvious patterns and important relationships between the survey variables. This knowledge can significantly affect the conclusions derived from a survey.
Key features:
• Provides an integrated, case-studies based approach to analysing customer survey data.
• Presents a general introduction to customer surveys, within an organization's business cycle.
• Contains classical techniques with modern and non standard tools.
• Focuses on probabilistic techniques from the area of statistics/data analysis and covers all major recent developments.
• Accompanied by a supporting website containing datasets and R scripts.
Customer survey specialists, quality managers and market researchers will benefit from this book as well as specialists in marketing, data mining and business intelligence fields.
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A Practical Guide to Cluster Randomised Trials in Health Services Research
by Sandra Eldridge
Part 120 of the Statistics in Practice series
Cluster randomised trials are trials in which groups (or clusters) of individuals are randomly allocated to different forms of treatment. In health care, these trials often compare different ways of managing a disease or promoting healthy living, in contrast to conventional randomised trials which randomise individuals to different treatments, classically comparing new drugs with a placebo. They are increasingly common in health services research. This book addresses the statistical, practical, and ethical issues arising from allocating groups of individuals, or clusters, to different interventions.
Key features:
• Guides readers through the stages of conducting a trial, from recruitment to reporting.
• Presents a wide range of examples with particular emphasis on trials in health services research and primary care, with both principles and techniques explained.
• Topics are specifically presented in the order in which investigators think about issues when they are designing a trial.
• Combines information on the latest developments in the field together with a practical guide to the design and implementation of cluster randomised trials.
• Explains principles and techniques through numerous examples including many from the authors own experience.
• Includes a wide range of references for those who wish to read further.
This book is intended as a practical guide, written for researchers from the health professions including doctors, psychologists, and allied health professionals, as well as statisticians involved in the design, execution, analysis and reporting of cluster randomised trials. Those with a more general interest will find the plentiful examples illuminating.
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Evidence Synthesis for Decision Making in Healthcare
by Nicky J. Welton
Part 127 of the Statistics in Practice series
In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish interventions that are both effective and cost-effective. Usually a single study will not fully address these issues and it is desirable to synthesize evidence from multiple sources. This book aims to provide a practical guide to evidence synthesis for the purpose of decision making, starting with a simple single parameter model, where all studies estimate the same quantity (pairwise meta-analysis) and progressing to more complex multi-parameter structures (including meta-regression, mixed treatment comparisons, Markov models of disease progression, and epidemiology models). A comprehensive, coherent framework is adopted and estimated using Bayesian methods.
Key features:
• A coherent approach to evidence synthesis from multiple sources.
• Focus is given to Bayesian methods for evidence synthesis that can be integrated within cost-effectiveness analyses in a probabilistic framework using Markov Chain Monte Carlo simulation.
• Provides methods to statistically combine evidence from a range of evidence structures.
• Emphasizes the importance of model critique and checking for evidence consistency.
• Presents numerous worked examples, exercises and solutions drawn from a variety of medical disciplines throughout the book.
• WinBUGS code is provided for all examples.
Evidence Synthesis for Decision Making in Healthcare is intended for health economists, decision modelers, statisticians and others involved in evidence synthesis, health technology assessment, and economic evaluation of health technologies.
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Statistical Monitoring of Complex Multivariate Processes
With Applications in Industrial Process Control
by Uwe Kruger
Part 135 of the Statistics in Practice series
The development and application of multivariate statistical techniques in process monitoring has gained substantial interest over the past two decades in academia and industry alike. Initially developed for monitoring and fault diagnosis in complex systems, such techniques have been refined and applied in various engineering areas, for example mechanical and manufacturing, chemical, electrical and electronic, and power engineering. The recipe for the tremendous interest in multivariate statistical techniques lies in its simplicity and adaptability for developing monitoring applications. In contrast, competitive model, signal or knowledge-based techniques showed their potential only whenever cost-benefit economics have justified the required effort in developing applications.
Statistical Monitoring of Complex Multivariate Processes presents recent advances in statistics-based process monitoring, explaining how these processes can now be used in areas such as mechanical and manufacturing engineering for example, in addition to the traditional chemical industry.
This book:
• Contains a detailed theoretical background of the component technology.
• Brings together a large body of work to address the field's drawbacks and develops methods for their improvement.
• Details cross-disciplinary utilization, exemplified by examples in chemical, mechanical and manufacturing engineering.
• Presents real life industrial applications, outlining deficiencies in the methodology and how to address them.
• Includes numerous examples, tutorial questions and homework assignments in the form of individual and team-based projects, to enhance the learning experience.
• Features a supplementary website including Matlab algorithms and data sets.
This book provides a timely reference text to the rapidly evolving area of multivariate statistical analysis for academics, advanced level students, and practitioners alike.
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Efficiency and Productivity Growth
Modelling in the Financial Services Industry
by Fotios Pasiouras
Part of the Statistics in Practice series
An authoritative introduction to efficiency and productivity analysis with applications in both the banking and finance industry
In light of the recent global financial crisis, several studies have examined the efficiency of financial institutions. A number of open questions remain and this book reviews recent issues and state-of-the-art techniques in the assessment of the efficiency and productivity of financial institutions. Written by an international team of experts, the first part of the book links efficiency with a variety of topics like Latin American banking, market discipline and governance, economics of scale, off-balance-sheet activities, productivity of foreign banks, mergers and acquisitions, and mutual fund ratings. The second part of the book compares existing techniques and state-of-the-art techniques in the bank efficiency literature, including among others, network data envelopment analysis and quantile regression.
The book is suitable for academics and professionals as well as postgraduate research students working in banking and finance.
Efficiency and Productivity Growth:
• Provides an authoritative introduction to efficiency and productivity analysis with applications in both the banking and mutual funds industry such as efficiency of Asian banks, cooperatives and not-for-profit credit associations.
• Explores contemporary research issues in the area of efficiency and productivity measurement in the financial sector.
• Evaluates the most suitable approaches to selecting inputs and outputs as well as selecting the most efficient techniques, such as parametric and non-parametric, to estimate the models.
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Introduction to Probability and Statistics for Ecosystem Managers
Simulation and Resampling
by Timothy C. Haas
Part of the Statistics in Practice series
Explores computer-intensive probability and statistics for ecosystem management decision making.
Simulation is an accessible way to explain probability and stochastic model behavior to beginners. This book introduces probability and statistics to future and practicing ecosystem managers by providing a comprehensive treatment of these two areas. The author presents a self-contained introduction for individuals involved in monitoring, assessing, and managing ecosystems and features intuitive, simulation-based explanations of probabilistic and statistical concepts. Mathematical programming details are provided for estimating ecosystem model parameters with Minimum Distance, a robust and computer-intensive method.
The majority of examples illustrate how probability and statistics can be applied to ecosystem management challenges. There are over 50 exercises—making this book suitable for a lecture course in a natural resource and/or wildlife management department, or as the main text in a program of self-study.
Key features:
• Reviews different approaches to wildlife and ecosystem management and inference.
• Uses simulation as an accessible way to explain probability and stochastic model behavior to beginners.
• Covers material from basic probability through to hierarchical Bayesian models and spatial/ spatio-temporal statistical inference.
• Provides detailed instructions for using R, along with complete R programs to recreate the output of the many examples presented.
• Provides an introduction to Geographic Information Systems (GIS) along with examples from Quantum GIS, a free GIS software package.
• A companion website featuring all R code and data used throughout the book.
• Solutions to all exercises are presented along with an online intelligent tutoring system that supports readers who are using the book for self-study.
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Multiple Imputation and Its Application
by James R. Carpenter
Part of the Statistics in Practice series
The most up-to-date edition of a bestselling guide to analyzing partially observed data.
In this comprehensively revised Second Edition of “Multiple Imputation and its Application”, a team of distinguished statisticians delivers an overview of the issues raised by missing data, the rationale for multiple imputation as a solution, and the practicalities of applying it in a multitude of settings.
With an accessible and carefully structured presentation aimed at quantitative researchers, Multiple Imputation and its Application is illustrated with a range of examples and offers key mathematical details. The book includes a wide range of theoretical and computer-based exercises, tested in the classroom, which are especially useful for users of R or Stata. Readers will find:
• A comprehensive overview of one of the most effective and popular methodologies for dealing with incomplete data sets
• Careful discussion of key concepts
• A range of examples illustrating the key ideas
• Practical advice on using multiple imputation
• Exercises and examples designed for use in the classroom and/or private study
Written for applied researchers looking to use multiple imputation with confidence, and for methods researchers seeking an accessible overview of the topic, “Multiple Imputation and its Application” will also earn a place in the libraries of graduate students undertaking quantitative analyses.
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The False Discovery Rate
Its Meaning, Interpretation And Application In Data Science
by N. W. Galwey
Part of the Statistics in Practice series
The False Discovery Rate
An essential tool for statisticians and data scientists seeking to interpret the vast troves of data that increasingly power our world
First developed in the 1990s, the False Discovery Rate (FDR) is a way of describing the rate at which null hypothesis testing produces errors. It has since become an essential tool for interpreting large datasets. In recent years, as datasets have become ever larger, and as the importance of 'big data' to scientific research has grown, the significance of the FDR has grown correspondingly.
The False Discovery Rate provides an analysis of the FDR's value as a tool, including why it should generally be preferred to the Bonferroni correction and other methods by which multiplicity can be accounted for. It offers a systematic overview of the FDR, its core claims, and its applications.
Readers of The False Discovery Rate will also find:
• Case studies throughout, rooted in real and simulated data sets
• Detailed discussion of topics including representation of the FDR on a Q–Q plot, consequences of non-monotonicity, and many more
• Wide-ranging analysis suited for a broad readership
The False Discovery Rate is ideal for Statistics and Data Science courses, and short courses associated with conferences. It is also useful as supplementary reading in courses in other disciplines that require the statistical interpretation of 'big data'. The book will also be of great value to statisticians and researchers looking to learn more about the FDR.
STATISTICS IN PRACTICE
A series of practical books outlining the use of statistical techniques in a wide range of applications areas:
• HUMAN AND BIOLOGICAL SCIENCES
• EARTH AND ENVIRONMENTAL SCIENCES
• INDUSTRY, COMMERCE AND FINANCE
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A Practical Approach to Quantitative Validation of Patient-Reported Outcomes
A Simulation-based Guide Using SAS
by Andrew G. Bushmakin
Part of the Statistics in Practice series
A Simulation-Based Guide Using SAS
In “A Practical Approach to Quantitative Validation of Patient-Reported Outcomes”, two distinguished researchers, with 50 years of collective research experience and hundreds of publications on patient-centered research, deliver a detailed and comprehensive exposition on the critical steps required for quantitative validation of patient-reported outcomes (PROs). The book provides an incisive and instructional explanation and discussion on major aspects of psychometric validation methodology on PROs, especially relevant for medical applications sponsored by the pharmaceutical industry, where SAS is the primary software, and evaluated in regulatory and other healthcare environments.
Central topics include test-retest reliability, exploratory and confirmatory factor analyses, construct and criterion validity, responsiveness and sensitivity, interpretation of PRO scores and findings, and meaningful within-patient change and clinical important difference. The authors provide step-by-step guidance while walking readers through how to structure data prior to a PRO analysis and demonstrate how to implement analyses with simulated examples grounded in real-life scenarios.
Readers will also find:
• A thorough introduction to patient-reported outcomes, including their definition, development, and psychometric validation
• Comprehensive explorations of the validation workflow, including discussions of clinical trials as a data source for validation and the validation workflow for single and multi-item scales
• In-depth discussions of key concepts related to a validation of a measurement scale
• Special attention is given to the US Food and Drug Administration (FDA) guidance on development and validation of the PROs, which lay the foundation and inspiration for the analytic methods executed
“A Practical Approach to Quantitative Validation of Patient-Reported Outcomes” is a required reference that will benefit psychometricians, statisticians, biostatisticians, epidemiologists, health service and public health researchers, outcome research scientists, regulators, and payers.
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Bayesian Biostatistics
by Emmanuel Lesaffre
Part of the Statistics in Practice series
The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets.
Through examples, exercises and a combination of introductory and more advanced chapters, this book provides an invaluable understanding of the complex world of biomedical statistics illustrated via a diverse range of applications taken from epidemiology, exploratory clinical studies, health promotion studies, image analysis and clinical trials.
Key Features:
• Provides an authoritative account of Bayesian methodology, from its most basic elements to its practical implementation, with an emphasis on healthcare techniques.
• Contains introductory explanations of Bayesian principles common to all areas of application.
• Presents clear and concise examples in biostatistics applications such as clinical trials, longitudinal studies, bioassay, survival, image analysis and bioinformatics.
• Illustrated throughout with examples using software including WinBUGS, OpenBUGS, SAS and various dedicated R programs.
• Highlights the differences between the Bayesian and classical approaches.
• Supported by an accompanying website hosting free software and case study guides.
“Bayesian Biostatistics” introduces the reader smoothly into the Bayesian statistical methods with chapters that gradually increase in level of complexity. Master students in biostatistics, applied statisticians and all researchers with a good background in classical statistics who have interest in Bayesian methods will find this book useful.
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Statistical Methods for Hospital Monitoring With R
by Anthony Morton
Part of the Statistics in Practice series
Hospitals monitoring is becoming more complex and is increasing both because staff want their data analysed and because of increasing mandated surveillance. This book provides a suite of functions in R, enabling scientists and data analysts working in infection management and quality improvement departments in hospitals, to analyse their often-non-independent data, which is frequently in the form of trended, over-dispersed and sometimes auto-correlated time series; this is often difficult to analyse using standard office software.
This book provides much-needed guidance on data analysis using R for the growing number of scientists in hospital departments who are responsible for producing reports, and who may have limited statistical expertise.
This book explores data analysis using R and is aimed at scientists in hospital departments who are responsible for producing reports, and who are involved in improving safety. Professionals working in the healthcare quality and safety community will also find this book of interest.
“Statistical Methods for Hospital Monitoring with R”:
• Provides functions to perform quality improvement and infection management data analysis.
• Explores the characteristics of complex systems, such as self-organisation and emergent behaviour, along with their implications for such activities as root-cause analysis and the Pareto principle that seek few key causes of adverse events.
• Provides a summary of key non-statistical aspects of hospital safety and easy to use functions.
• Provides R scripts in an accompanying web site enabling analyses to be performed by the reader http://www.wiley.com/go/hospital_monitoring
• Covers issues that will be of increasing importance in the future, such as, generalised additive models, and complex systems, networks and power laws.
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Spatial Analysis Along Networks
Statistical and Computational Methods
by Atsuyuki Okabe
Part of the Statistics in Practice series
In the real world, there are numerous and various events that occur on and alongside networks, including the occurrence of traffic accidents on highways, the location of stores alongside roads, the incidence of crime on streets and the contamination along rivers. In order to carry out analyses of those events, the researcher needs to be familiar with a range of specific techniques. “Spatial Analysis Along Networks” provides a practical guide to the necessary statistical techniques and their computational implementation.
Each chapter illustrates a specific technique, from Stochastic Point Processes on a Network and Network Voronoi Diagrams, to Network K-function and Point Density Estimation Methods, and the Network Huff Model. The authors also discuss and illustrate the undertaking of the statistical tests described in a Geographical Information System (GIS) environment as well as demonstrating the user-friendly free software package SANET.
“Spatial Analysis Along Networks”:
• Presents a much-needed practical guide to statistical spatial analysis of events on and alongside a network, in a logical, user-friendly order.
• Introduces the preliminary methods involved, before detailing the advanced, computational methods, enabling the readers a complete understanding of the advanced topics.
• Dedicates a separate chapter to each of the major techniques involved.
• Demonstrates the practicalities of undertaking the tests described in the book, using a GIS.
• Is supported by a supplementary website, providing readers with a link to the free software package SANET, so they can execute the statistical methods described in the book.
Students and researchers studying spatial statistics, spatial analysis, geography, GIS, OR, traffic accident analysis, criminology, retail marketing, facility management and ecology will benefit from this book.
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Statistical Methods for Evaluating Safety in Medical Product Development
by Various Authors
Part of the Statistics in Practice series
This book gives professionals in clinical research valuable information on the challenging issues of the design, execution, and management of clinical trials, and how to resolve these issues effectively. It also provides understanding and practical guidance on the application of contemporary statistical methods to contemporary issues in safety evaluation during medical product development. Each chapter provides sufficient detail to the reader to undertake the design and analysis of experiments at various stages of product development, including comprehensive references to the relevant literature.
• Provides a guide to statistical methods and application in medical product development
• Assists readers in undertaking design and analysis of experiments at various stages of product development
• Features case studies throughout the book, as well as, SAS and R code.
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Statistics and the Evaluation of Evidence for Forensic Scientists
by Colin Aitken
Part of the Statistics in Practice series
The third edition of “Statistics and the Evaluation of Evidence for Forensic Scientists” is fully updated to provide the latest research and developments in the use of statistical techniques to evaluate and interpret evidence. Courts are increasingly aware of the importance of proper evidence assessment when there is an element of uncertainty. Because of the increasing availability of data, the role of statistical and probabilistic reasoning is gaining a higher profile in criminal cases. That's why lawyers, forensic scientists, graduate students, and researchers will find this book an essential resource, one which explores how forensic evidence can be evaluated and interpreted statistically. It's written as an accessible source of information for all those with an interest in the evaluation and interpretation of forensic scientific evidence.
• Discusses the entire chain of reasoning—from evidence pre-assessment to court presentation;
• Includes material for the understanding of evidence interpretation for single and multiple trace evidence;
• Provides real examples and data for improved understanding.
Since the first edition of this book was published in 1995, this respected series has remained a leading resource in the statistical evaluation of forensic evidence. It shares knowledge from authors in the fields of statistics and forensic science who are international experts in the area of evidence evaluation and interpretation. This book helps people to deal with uncertainty related to scientific evidence and propositions. It introduces a method of reasoning that shows how to update beliefs coherently and to act rationally. In this edition, readers can find new information on the topics of elicitation, subjective probabilities, decision analysis, and cognitive bias, all discussed in a Bayesian framework.
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Modern Industrial Statistics
With Applications in R, MINITAB, and JMP
by Ron S. Kenett
Part of the Statistics in Practice series
The new edition of the prime reference on the tools of statistics used in industry and services, integrating theoretical, practical, and computer-based approaches.
“Modern Industrial Statistics” is a leading reference and guide to the statistics tools widely used in industry and services. 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.
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Numerical Methods in Finance and Economics
A MATLAB-Based Introduction
by Paolo Brandimarte
Part of the Statistics in Practice series
A state-of-the-art introduction to the powerful mathematical and statistical tools used in the field of finance.
The use of mathematical models and numerical techniques is a practice employed by a growing number of applied mathematicians working on applications in finance. Reflecting this development, “Numerical Methods in Finance and Economics: A MATLAB-Based Introduction”, Second Edition bridges the gap between financial theory and computational practice while showing readers how to utilize MATLAB-the powerful numerical computing environment-for financial applications.
The author provides an essential foundation in finance and numerical analysis in addition to background material for students from both engineering and economics perspectives. A wide range of topics is covered, including standard numerical analysis methods, Monte Carlo methods to simulate systems affected by significant uncertainty, and optimization methods to find an optimal set of decisions.
Among this book's most outstanding features is the integration of MATLAB, which helps students and practitioners solve relevant problems in finance, such as portfolio management and derivatives pricing. This tutorial is useful in connecting theory with practice in the application of classical numerical methods and advanced methods, while illustrating underlying algorithmic concepts in concrete terms.
Newly featured in the Second Edition:
* In-depth treatment of Monte Carlo methods with due attention paid to variance reduction strategies
* New appendix on AMPL in order to better illustrate the optimization models in Chapters 11 and 12
* New chapter on binomial and trinomial lattices
* Additional treatment of partial differential equations with two space dimensions
* Expanded treatment within the chapter on financial theory to provide a more thorough background for engineers not familiar with finance
* New coverage of advanced optimization methods and applications later in the text
“Numerical Methods in Finance and Economics: A MATLAB-Based Introduction”, Second Edition presents basic treatments and more specialized literature, and it also uses algebraic languages, such as AMPL, to connect the pencil-and-paper statement of an optimization model with its solution by a software library. Offering computational practice in both financial engineering and economics fields, this book equips practitioners with the necessary techniques to measure and manage risk.
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Spatio-Temporal Design
Advances in Efficient Data Acquisition
by Various Authors
Part of the Statistics in Practice series
A state-of-the-art presentation of optimum spatio-temporal sampling design-bridging classic ideas with modern statistical modeling concepts and the latest computational methods.
Spatio-temporal Design presents a comprehensive state-of-the-art presentation combining both classical and modern treatments of network design and planning for spatial and spatio-temporal data acquisition. A common problem set is interwoven throughout the chapters, providing various perspectives to illustrate a complete insight to the problem at hand.
Motivated by the high demand for statistical analysis of data that takes spatial and spatio-temporal information into account, this book incorporates ideas from the areas of time series, spatial statistics and stochastic processes, and combines them to discuss optimum spatio-temporal sampling design.
“Spatio-temporal Design: Advances in Efficient Data Acquisition”:
• Provides an up-to-date account of how to collect space-time data for monitoring, with a focus on statistical aspects and the latest computational methods
• Discusses basic methods and distinguishes between design and model-based approaches to collecting space-time data.
• Features model-based frequentist design for univariate and multivariate geostatistics, and second-phase spatial sampling.
• Integrates common data examples and case studies throughout the book in order to demonstrate the different approaches and their integration.
• Includes real data sets, data generating mechanisms and simulation scenarios.
• Accompanied by a supporting website featuring R code.
“Spatio-temporal Design” presents an excellent book for graduate level students as well as a valuable reference for researchers and practitioners in the fields of applied mathematics, engineering, and the environmental and health sciences.
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Crossover Designs
Testing, Estimation, and Sample Size
by Kung-Jong Lui
Part of the Statistics in Practice series
A comprehensive and practical resource for analyses of crossover designs
For ethical reasons, it is vital to keep the number of patients in a clinical trial as low as possible. As evidenced by extensive research publications, crossover design can be a useful and powerful tool to reduce the number of patients needed for a parallel group design in studying treatments for non-curable chronic diseases.
This book introduces commonly-used and well-established statistical tests and estimators in epidemiology that can easily be applied to hypothesis testing and estimation of the relative treatment effect for various types of data scale in crossover designs. Models with distribution-free random effects are assumed and hence most approaches considered here are semi-parametric. The book provides clinicians and biostatisticians with the exact test procedures and exact interval estimators, which are applicable even when the number of patients in a crossover trial is small. Systematic discussion on sample size determination is also included, which will be a valuable resource for researchers involved in crossover trial design.
Key features:
• Provides exact test procedures and interval estimators, which are especially of use in small-sample cases.
• Presents most test procedures and interval estimators in closed-forms, enabling readers to calculate them by use of a pocket calculator or commonly-used statistical packages.
• Each chapter is self-contained, allowing the book to be used a reference resource.
• Uses real-life examples to illustrate the practical use of test procedures and estimators
• Provides extensive exercises to help readers appreciate the underlying theory, learn other relevant test procedures and understand how to calculate the required sample size.
“Crossover Designs: Testing, Estimation and Sample Size” will be a useful resource for researchers from biostatistics, as well as pharmaceutical and clinical sciences. It can also be used as a textbook or reference for graduate students studying clinical experiments.
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Statistical Issues in Drug Development
by Stephen S. Senn
Part of the Statistics in Practice series
The revised third edition of “Statistical Issues in Drug Development” delivers an insightful treatment of the intersection between statistics and the life sciences. The book offers readers new discussions of crucial topics, including cluster randomization, historical controls, responder analysis, studies in children, post-hoc tests, estimands, publication bias, the replication crisis, and many more.
This work presents the major statistical issues in drug development in a way that is accessible and comprehensible to life scientists working in the field and takes pains not to gloss over significant disagreements in the field of statistics, while encouraging communication between the statistical and life sciences disciplines. In addition to new material on topics like invalid inversion, severity, random effects in network meta-analysis, and explained variation, readers will benefit from the inclusion of:
• A thorough introduction to basic topics in drug development and statistics, including the role played by statistics in drug development
• An exploration of the four views of statistics in drug development, including the historical, methodological, technical, and professional
• An examination of debatable and controversial topics in drug development, including the allocation of treatments to patients in clinical trials, baselines and covariate information, and the measurement of treatment effects
Perfect for life scientists and other professionals working in the field of drug development, “Statistical Issues in Drug Development” is the ideal resource for anyone seeking a one-stop reference to enhance their understanding of the use of statistics during drug development.
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Clinical Trials With Missing Data
A Guide for Practitioners
by Michael O'Kelly
Part of the Statistics in Practice series
This book provides practical guidance for statisticians, clinicians, and researchers involved in clinical trials in the biopharmaceutical industry, medical and public health organisations. Academics and students needing an introduction to handling missing data will also find this book invaluable.
The authors describe how missing data can affect the outcome and credibility of a clinical trial, show by examples how a clinical team can work to prevent missing data, and present the reader with approaches to address missing data effectively.
The book is illustrated throughout with realistic case studies and worked examples, and presents clear and concise guidelines to enable good planning for missing data. The authors show how to handle missing data in a way that is transparent and easy to understand for clinicians, regulators and patients. New developments are presented to improve the choice and implementation of primary and sensitivity analyses for missing data. Many SAS code examples are included — the reader is given a toolbox for implementing analyses under a variety of assumptions.
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Data Monitoring Committees in Clinical Trials
A Practical Perspective
by Susan S. Ellenberg
Part of the Statistics in Practice series
The authoritative guide for Data Monitoring Committees-fully revised and updated.
The number of clinical trials sponsored by government agencies and pharmaceutical companies has grown in recent years, prompting an increased need for interim monitoring of data on safety and efficacy. Data Monitoring Committees (DMCs) are an essential component of many clinical trials, safeguarding trial participants and protecting the credibility and validity of the study. “Data Monitoring Committees in Clinical Trials: A Practical Perspective”, 2nd Edition offers practical advice for those managing and conducting clinical trials and serving on Data Monitoring Committees, providing a practical overview of the establishment, purpose, and responsibilities of these committees.
Examination of topics such as the composition and independence of DMCs, statistical, philosophical and ethical considerations, and determining when a DMC is needed, presents readers with a comprehensive foundational knowledge of clinical trial oversight.
Providing recent examples to illustrate DMC principles, this fully-updated guide reflects current developments and practices in clinical trial oversight and offers expanded coverage of emerging issues and challenges in the field. This new second edition covers the most current information on DMC policies, issues in monitoring trials using new designs, and recent trial publications relevant to DMC decision-making.
• Presents practical advice for those managing and conducting clinical trials and serving on Data Monitoring Committees
• Illustrates the types of challenging issues Data Monitoring Committees face in practical situations
• Provides updated and expanded coverage of topics including regulatory and funding agency guidelines and trial designs and their associated demands and limitations
• Includes a new chapter addressing legal issues that affect DMC members and discusses general litigation concerns relevant to clinical research
• Expands treatment of current journal publications addressing DMC issues
“Data Monitoring Committees in Clinical Trials: A Practical Perspective”, 2nd Edition is a must-have text for anyone engaged in DMC activities as well as trial sponsors, clinical trial researchers, regulatory and bioethics professionals, and those associated with clinical trials in academic, government and industry settings.
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A Practical Guide to Designing Phase II Trials in Oncology
by Sarah R. Brown
Part of the Statistics in Practice series
How to identify optimal phase II trial designs
Providing a practical guide containing the information needed to make crucial decisions regarding phase II trial designs, “A Practical Guide to Designing Phase II Trials in Oncology” sets forth specific points for consideration between the statistician and clinician when designing a phase II trial, including issues such as how the treatment works, choice of outcome measure and randomization, and considering both academic and industry perspectives. A comprehensive and systematic library of available phase II trial designs is included, saving time otherwise spent considering multiple manuscripts, and real-life practical examples of using this approach to design phase II trials in cancer are given.
“A Practical Guide to Designing Phase II Trials in Oncology”:
• Offers a structured and practical approach to phase II trial design
• Considers trial design from both an academic and industry perspective
• Includes a structured library of available phase II trial designs
• Is relevant to both clinical and statistical researchers at all levels
• Includes real life examples of applying this approach
For those new to trial design, “A Practical Guide to Designing Phase II Trials in Oncology” will be a unique and practical learning tool, providing an introduction to the concepts behind informed decision making in phase II trials. For more experienced practitioners, the book will offer an overview of new, less familiar approaches to phase II trial design, providing alternative options to those which they may have previously used.
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How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research
by Michael J. Campbell
Part of the Statistics in Practice series
A complete guide to understanding cluster randomised trials
Written by two researchers with extensive experience in the field, this book presents a complete guide to the design, analysis and reporting of cluster randomised trials. It spans a wide range of applications: trials in developing countries, trials in primary care, trials in the health services. A key feature is the use of R code and code from other popular packages to plan and analyse cluster trials, using data from actual trials. The book contains clear technical descriptions of the models used, and considers in detail the ethics involved in such trials and the problems in planning them. For readers and students who do not intend to run a trial but wish to be a critical reader of the literature, there are sections on the CONSORT statement, and exercises in reading published trials.
• Written in a clear, accessible style
• Features real examples taken from the authors' extensive practitioner experience of designing and analysing clinical trials
• Demonstrates the use of R, Stata and SPSS for statistical analysis
• Includes computer code so the reader can replicate all the analyses
• Discusses neglected areas such as ethics and practical issues in running cluster randomised trials
How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research provides an excellent reference tool and can be read with profit by statisticians, health services researchers, systematic reviewers and critical readers of cluster randomised trials.
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Multiple Imputation and its Application
by James Carpenter
Part of the Statistics in Practice series
A practical guide to analysing partially observed data.
Collecting, analysing and drawing inferences from data is central to research in the medical and social sciences. Unfortunately, it is rarely possible to collect all the intended data. The literature on inference from the resulting incomplete data is now huge and continues to grow both as methods are developed for large and complex data structures, and as increasing computer power and suitable software enable researchers to apply these methods.
This book focuses on a particular statistical method for analysing and drawing inferences from incomplete data, called Multiple Imputation (MI). MI is attractive because it is both practical and widely applicable. The authors aim is to clarify the issues raised by missing data, describing the rationale for MI, the relationship between the various imputation models and associated algorithms and its application to increasingly complex data structures.
Multiple Imputation and its Application:
• Discusses the issues raised by the analysis of partially observed data, and the assumptions on which analyses rest.
• Presents a practical guide to the issues to consider when analysing incomplete data from both observational studies and randomized trials.
• Provides a detailed discussion of the practical use of MI with real-world examples drawn from medical and social statistics.
• Explores handling non-linear relationships and interactions with multiple imputation, survival analysis, multilevel multiple imputation, sensitivity analysis via multiple imputation, using non-response weights with multiple imputation and doubly robust multiple imputation.
“Multiple Imputation and its Application” is aimed at quantitative researchers and students in the medical and social sciences with the aim of clarifying the issues raised by the analysis of incomplete data, outlining the rationale for MI and describing how to consider and address the issues that arise in its application.
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Network Meta-Analysis for Decision-Making
by Sofia Dias
Part of the Statistics in Practice series
A practical guide to network meta-analysis with examples and code.
In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish which interventions are effective and cost-effective. Often a single study will not provide the answers and it is desirable to synthesise evidence from multiple sources, usually randomised controlled trials. This book takes an approach to evidence synthesis that is specifically intended for decision making when there are two or more treatment alternatives being evaluated, and assumes that the purpose of every synthesis is to answer the question "for this pre-identified population of patients, which treatment is 'best'?"
A comprehensive, coherent framework for network meta-analysis (mixed treatment comparisons) is adopted and estimated using Bayesian Markov Chain Monte Carlo methods implemented in the freely available software WinBUGS. Each chapter contains worked examples, exercises, solutions and code that may be adapted by readers to apply to their own analyses.
This book can be used as an introduction to evidence synthesis and network meta-analysis, its key properties and policy implications. Examples and advanced methods are also presented for the more experienced reader.
• Methods used throughout this book can be applied consistently: model critique and checking for evidence consistency are emphasised.
• Methods are based on technical support documents produced for NICE Decision Support Unit, which support the NICE Methods of Technology Appraisal.
• Code presented is also the basis for the code used by the ISPOR Task Force on Indirect Comparisons.
• Includes extensive carefully worked examples, with thorough explanations of how to set out data for use in WinBUGS and how to interpret the output.
“Network Meta-Analysis for Decision Making” will be of interest to decision makers, medical statisticians, health economists, and anyone involved in Health Technology Assessment including the pharmaceutical industry.
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Exploratory Subgroup Analyses in Clinical Research
by Gerd Rosenkranz
Part of the Statistics in Practice series
This essential guide on subgroup analyses in the emerging area of personalized medicine covers the issues of subgroup analyses from a practical and a theoretical/methodological point of view. The practical part introduces the issues using examples from the literature where subgroup analyses led to unexpected or difficult-to-interpret results, which have been interpreted differently by different stakeholders. On the technical side, the book addresses selection and selection bias variance reduction by borrowing information from the full population in estimating a subgroup effect. To this end, subgroup analysis will be linked to statistical modelling, and subgroup selection to model selection. This connection makes the techniques developed for model selection applicable to subgroup analysis.
Beginning with a history of subgroup analysis, “Exploratory Subgroup Analyses in Clinical Research” offers chapters that cover: objectives and current practice of subgroup analyses; pitfalls of subgroup analyses; subgroup analysis and modeling; hierarchical models in subgroup analysis; and selection bias in regression. It also looks at the predicted individual treatment effect and offers an outlook of the topic in its final chapter.
• Focuses on the statistical aspects of subgroup analysis
• Filled with classroom and conference-workshop tested material
• Written by a leading expert in the field of subgroup analysis
“Exploratory Subgroup Analyses in Clinical Research” is an ideal book for medical statisticians and biostatisticians and will greatly benefit physicians and researchers interested in personalized medicine.
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Individual Participant Data Meta-Analysis
A Handbook for Healthcare Research
by Various Authors
Part of the Statistics in Practice series
“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 Detailed examples and case studies are provided throughout.
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Weight-of-Evidence for Forensic DNA Profiles
by David J. Balding
Part of the Statistics in Practice series
DNA evidence is widely used in the modern justice system. Statistical methodology plays a key role in ensuring that this evidence is collected, interpreted, analysed and presented correctly. This book is a guide to assessing DNA evidence and presenting that evidence in a courtroom setting. It offers practical guidance to forensic scientists with little dependence on mathematical ability, and provides the scientist with the understanding they require to apply the methods in their work. Since the publication of the first edition of this book in 2005 there have been many incremental changes, and one dramatic change which is the emergence of low template DNA (LTDNA) profiles. This second edition is edited and expanded to cover the basics of LTDNA technology. The author's own open-source R code like LTD is described and used for worked examples in the book. Commercial and free software are also covered.
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Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science
by Franco Taroni
Part of the Statistics in Practice series
Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system.
“Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science” provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks.
• Includes self-contained introductions to probability and decision theory.
• Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models.
• Features implementation of the methodology with reference to commercial and academically available software.
• Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases.
• Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning.
• Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them.
• Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background.
• Includes a foreword by Ian Evett.
The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.
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Applied Mixed Models in Medicine
by Helen Brown
Part of the Statistics in Practice series
A fully updated edition of this key text on mixed models, focusing on applications in medical research.
The application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry. A mixed model allows the incorporation of both fixed and random variables within a statistical analysis, enabling efficient inferences and more information to be gained from the data. There have been many recent advances in mixed modelling, particularly regarding the software and applications. This third edition of Brown and Prescott's groundbreaking text provides an update on the latest developments and includes guidance on the use of current SAS techniques across a wide range of applications.
• Presents an overview of the theory and applications of mixed models in medical research, including the latest developments and new sections on incomplete block designs and the analysis of bilateral data.
• Easily accessible to practitioners in any area where mixed models are used, including medical statisticians and economists.
• Includes numerous examples using real data from medical and health research, and epidemiology, illustrated with SAS code and output.
• Features the new version of SAS, including new graphics for model diagnostics and the procedure PROC MCMC.
• Supported by a website featuring computer code, data sets, and further material.
This third edition will appeal to applied statisticians working in medical research and the pharmaceutical industry, as well as teachers and students of statistics courses in mixed models. The book will also be of great value to a broad range of scientists, particularly those working in the medical and pharmaceutical areas.
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