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Bayesian Analysis with R for Drug Development Concepts Algorithms and Case Studies

Bayesian Analysis with R for Drug Development Concepts Algorithms and Case Studies

Drug development is an iterative process. The recent publications of regulatory guidelines further entail a lifecycle approach. Blending data from disparate sources the Bayesian approach provides a flexible framework for drug development. Despite its advantages the uptake of Bayesian methodologies is lagging behind in the field of pharmaceutical development. Written specifically for pharmaceutical practitioners Bayesian Analysis with R for Drug Development: Concepts Algorithms and Case Studies describes a wide range of Bayesian applications to problems throughout pre-clinical clinical and Chemistry Manufacturing and Control (CMC) development. Authored by two seasoned statisticians in the pharmaceutical industry the book provides detailed Bayesian solutions to a broad array of pharmaceutical problems. Features Provides a single source of information on Bayesian statistics for drug development Covers a wide spectrum of pre-clinical clinical and CMC topics Demonstrates proper Bayesian applications using real-life examples Includes easy-to-follow R code with Bayesian Markov Chain Monte Carlo performed in both JAGS and Stan Bayesian software platforms Offers sufficient background for each problem and detailed description of solutions suitable for practitioners with limited Bayesian knowledge Harry Yang Ph. D. is Senior Director and Head of Statistical Sciences at AstraZeneca. He has 24 years of experience across all aspects of drug research and development and extensive global regulatory experiences. He has published 6 statistical books 15 book chapters and over 90 peer-reviewed papers on diverse scientific and statistical subjects including 15 joint statistical works with Dr. Novick. He is a frequent invited speaker at national and international conferences. He also developed statistical courses and conducted training at the FDA and USP as well as Peking University. Steven Novick Ph. D. is Director of Statistical Sciences at AstraZeneca. He has extensively contributed statistical methods to the biopharmaceutical literature. Novick is a skilled Bayesian computer programmer and is frequently invited to speak at conferences having developed and taught courses in several areas including drug-combination analysis and Bayesian methods in clinical areas. Novick served on IPAC-RS and has chaired several national statistical conferences. | Bayesian Analysis with R for Drug Development Concepts Algorithms and Case Studies

GBP 38.99
1

The Analysis of Time Series An Introduction with R

The Analysis of Time Series An Introduction with R

This new edition of this classic title now in its seventh edition presents a balanced and comprehensive introduction to the theory implementation and practice of time series analysis. The book covers a wide range of topics including ARIMA models forecasting methods spectral analysis linear systems state-space models the Kalman filters nonlinear models volatility models and multivariate models. | The Analysis of Time Series An Introduction with R

GBP 69.99
1

Handbook of Regression Modeling in People Analytics With Examples in R and Python

Handbook of Regression Modeling in People Analytics With Examples in R and Python

Despite the recent rapid growth in machine learning and predictive analytics many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best ‘swiss army knife’ we have for answering these kinds of questions. This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python ranging from simple hypothesis testing to advanced multivariate modelling. Although it is primarily focused on examples related to the analysis of people and talent the methods easily transfer to any discipline. The book hits a ‘sweet spot’ where there is just enough mathematical theory to support a strong understanding of the methods but with a step-by-step guide and easily reproducible examples and code so that the methods can be put into practice immediately. This makes the book accessible to a wide readership from public and private sector analysts and practitioners to students and researchers. Key Features: 16 accompanying datasets across a wide range of contexts (e. g. academic corporate sports marketing) Clear step-by-step instructions on executing the analyses Clear guidance on how to interpret results Primary instruction in R but added sections for Python coders Discussion exercises and data exercises for each of the main chapters Final chapter of practice material and datasets ideal for class homework or project work. | Handbook of Regression Modeling in People Analytics With Examples in R and Python

GBP 66.99
1

The BUGS Book A Practical Introduction to Bayesian Analysis

The BUGS Book A Practical Introduction to Bayesian Analysis

Bayesian statistical methods have become widely used for data analysis and modelling in recent years and the BUGS software has become the most popular software for Bayesian analysis worldwide. Authored by the team that originally developed this software The BUGS Book provides a practical introduction to this program and its use. The text presents complete coverage of all the functionalities of BUGS including prediction missing data model criticism and prior sensitivity. It also features a large number of worked examples and a wide range of applications from various disciplines. The book introduces regression models techniques for criticism and comparison and a wide range of modelling issues before going into the vital area of hierarchical models one of the most common applications of Bayesian methods. It deals with essentials of modelling without getting bogged down in complexity. The book emphasises model criticism model comparison sensitivity analysis to alternative priors and thoughtful choice of prior distributions all those aspects of the art of modelling that are easily overlooked in more theoretical expositions. More pragmatic than ideological the authors systematically work through the large range of tricks that reveal the real power of the BUGS software for example dealing with missing data censoring grouped data prediction ranking parameter constraints and so on. Many of the examples are biostatistical but they do not require domain knowledge and are generalisable to a wide range of other application areas. Full code and data for examples exercises and some solutions can be found on the book‘s website. | The BUGS Book A Practical Introduction to Bayesian Analysis

GBP 180.00
1

NoSQL Database for Storage and Retrieval of Data in Cloud

NoSQL Database for Storage and Retrieval of Data in Cloud

This book discusses the advanced databases for the cloud-based application known as NoSQL. It will explore the recent advancements in NoSQL database technology. Chapters on structured unstructured and hybrid databases will be included to explore bigdata analytics bigdata storage and processing. The book is likely to cover a wide range of topics such as cloud computing social computing bigdata and advanced databases processing techniques. | NoSQL Database for Storage and Retrieval of Data in Cloud

GBP 120.00
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Meta-analysis and Combining Information in Genetics and Genomics

Meta-analysis and Combining Information in Genetics and Genomics

Novel Techniques for Analyzing and Combining Data from Modern Biological StudiesBroadens the Traditional Definition of Meta-Analysis With the diversity of data and meta-data now available there is increased interest in analyzing multiple studies beyond statistical approaches of formal meta-analysis. Covering an extensive range of quantitative information combination methods Meta-analysis and Combining Information in Genetics and Genomics looks at how to analyze multiple studies from a broad perspective. After presenting the basic ideas and tools of meta-analysis the book addresses the combination of similar data types: genotype data from genome-wide linkage scans and data derived from microarray gene expression experiments. The expert contributors show how some data combination problems can arise even within the same basic framework and offer solutions to these problems. They also discuss the combined analysis of different data types giving readers an opportunity to see data combination approaches in action across a wide variety of genome-scale investigations. As heterogeneous data sets become more common biological understanding will be significantly aided by jointly analyzing such data using fundamentally sound statistical methodology. This book provides many novel techniques for analyzing data from modern biological studies that involve multiple data sets either of the same type or multiple data sources.

GBP 69.99
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Multilevel Modeling Using Mplus

Multilevel Modeling Using Mplus

This book is designed primarily for upper level undergraduate and graduate level students taking a course in multilevel modelling and/or statistical modelling with a large multilevel modelling component. The focus is on presenting the theory and practice of major multilevel modelling techniques in a variety of contexts using Mplus as the software tool and demonstrating the various functions available for these analyses in Mplus which is widely used by researchers in various fields including most of the social sciences. In particular Mplus offers users a wide array of tools for latent variable modelling including for multilevel data. | Multilevel Modeling Using Mplus

GBP 180.00
1

Introduction to Linear Algebra

Introduction to Linear Algebra

Linear algebra provides the essential mathematical tools to tackle all the problems in Science. Introduction to Linear Algebra is primarily aimed at students in applied fields (e. g. Computer Science and Engineering) providing them with a concrete rigorous approach to face and solve various types of problems for the applications of their interest. This book offers a straightforward introduction to linear algebra that requires a minimal mathematical background to read and engage with. Features Presented in a brief informative and engaging style Suitable for a wide broad range of undergraduates Contains many worked examples and exercises

GBP 44.99
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Cloud IoT Systems for Smart Agricultural Engineering

Cloud IoT Systems for Smart Agricultural Engineering

Agriculture plays a vital role in a country’s growth. Modern-day technologies drive every domain toward smart systems. The use of traditional agricultural procedures to satisfy modern-day requirements is a challenging task. Cloud IoT Systems for Smart Agricultural Engineering provides substantial coverage of various challenges of the agriculture domain through modern technologies such as the Internet of Things (IoT) cloud computing and many more. This book offers various state-of-the-art procedures to be deployed in a wide range of agricultural activities. The concepts are discussed with the necessary implementations and clear examples. Necessary illustrations are depicted in the chapters to ensure the effective delivery of the proposed concepts. It presents the rapid advancement of the technologies in the existing agricultural model by applying the cloud IoT techniques. A wide variety of novel architectural solutions are discussed in various chapters of this book. This book provides comprehensive coverage of the most essential topics including: New approaches on urban and vertical farming Smart crop management for Indian farmers Smart livestock management Precision agriculture using geographical information systems Machine learning techniques combined with IoT for smart agriculture Effective use of drones in smart agriculture This book provides solutions for the diverse domain of problems in agricultural engineering. It can be used at the basic and intermediary levels for agricultural science and engineering graduate students researchers and practitioners.

GBP 145.00
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Fitting Statistical Distributions The Generalized Lambda Distribution and Generalized Bootstrap Methods

Fitting Statistical Distributions The Generalized Lambda Distribution and Generalized Bootstrap Methods

Throughout the physical and social sciences researchers face the challenge of fitting statistical distributions to their data. Although the study of statistical modelling has made great strides in recent years the number and variety of distributions to choose from-all with their own formulas tables diagrams and general properties-continue to create problems. For a specific application which of the dozens of distributions should one use? What if none of them fit well?Fitting Statistical Distributions helps answer those questions. Focusing on techniques used successfully across many fields the authors present all of the relevant results related to the Generalized Lambda Distribution (GLD) the Generalized Bootstrap (GB) and Monte Carlo simulation (MC). They provide the tables algorithms and computer programs needed for fitting continuous probability distributions to data in a wide variety of circumstances-covering bivariate as well as univariate distributions and including situations where moments do not exist. Regardless of your specific field-physical science social science or statistics practitioner or theorist-Fitting Statistical Distributions is required reading. It includes wide-ranging applications illustrating the methods in practice and offers proofs of key results for those involved in theoretical development. Without it you may be using obsolete methods wasting time and risking incorrect results. | Fitting Statistical Distributions The Generalized Lambda Distribution and Generalized Bootstrap Methods

GBP 59.99
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Randomization Bootstrap and Monte Carlo Methods in Biology

Randomization Bootstrap and Monte Carlo Methods in Biology

Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. Like its bestselling predecessors the fourth edition of Randomization Bootstrap and Monte Carlo Methods in Biology illustrates a large number of statistical methods with an emphasis on biological applications. The focus is now on the use of randomization bootstrapping and Monte Carlo methods in constructing confidence intervals and doing tests of significance. The text provides comprehensive coverage of computer-intensive applications with data sets available online. Features Presents an overview of computer-intensive statistical methods and applications in biology Covers a wide range of methods including bootstrap Monte Carlo ANOVA regression and Bayesian methods Makes it easy for biologists researchers and students to understand the methods used Provides information about computer programs and packages to implement calculations particularly using R code Includes a large number of real examples from a range of biological disciplines Written in an accessible style with minimal coverage of theoretical details this book provides an excellent introduction to computer-intensive statistical methods for biological researchers. It can be used as a course text for graduate students as well as a reference for researchers from a range of disciplines. The detailed worked examples of real applications will enable practitioners to apply the methods to their own biological data.

GBP 44.99
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Handbook of Mixture Analysis

Handbook of Mixture Analysis

Mixture models have been around for over 150 years and they are found in many branches of statistical modelling as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate continuous or categorical cross-sectional time series networks and much more. Mixture analysis is a very active research topic in statistics and machine learning with new developments in methodology and applications taking place all the time. The Handbook of Mixture Analysis is a very timely publication presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics including the EM algorithm Bayesian mixture models model-based clustering high-dimensional data hidden Markov models and applications in finance genomics and astronomy. Features: Provides a comprehensive overview of the methods and applications of mixture modelling and analysis Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications Contains many worked examples using real data together with computational implementation to illustrate the methods described Includes contributions from the leading researchers in the field The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field whether they are developing new methodology or applying the models to real scientific problems.

GBP 56.99
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Wavelet Analysis Basic Concepts and Applications

Wavelet Analysis Basic Concepts and Applications

Wavelet Analysis: Basic Concepts and Applications provides a basic and self-contained introduction to the ideas underpinning wavelet theory and its diverse applications. This book is suitable for master’s or PhD students senior researchers or scientists working in industrial settings where wavelets are used to model real-world phenomena and data needs (such as finance medicine engineering transport images signals etc. ). Features: Offers a self-contained discussion of wavelet theory Suitable for a wide audience of post-graduate students researchers practitioners and theorists Provides researchers with detailed proofs Provides guides for readers to help them understand and practice wavelet analysis in different areas | Wavelet Analysis Basic Concepts and Applications

GBP 44.99
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Statistical Modeling and Machine Learning for Molecular Biology

Statistical Modeling and Machine Learning for Molecular Biology

Molecular biologists are performing increasingly large and complicated experiments but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs ortholog identification motif finding inference of population structure protein fold prediction and many more. The book takes a pragmatic approach focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics. | Statistical Modeling and Machine Learning for Molecular Biology

GBP 180.00
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Handbook of Biomarkers and Precision Medicine

Handbook of Biomarkers and Precision Medicine

The field of Biomarkers and Precision Medicine in drug development is rapidly evolving and this book presents a snapshot of exciting new approaches. By presenting a wide range of biomarker applications discussed by knowledgeable and experienced scientists readers will develop an appreciation of the scope and breadth of biomarker knowledge and find examples that will help them in their own work. Maria Freire Foundation for the National Institutes of HealthHandbook of Biomarkers and Precision Medicine provides comprehensive insights into biomarker discovery and development which has driven the new era of Precision Medicine. A wide variety of renowned experts from government academia teaching hospitals biotechnology and pharmaceutical companies share best practices examples and exciting new developments. The handbook aims to provide in-depth knowledge to research scientists students and decision makers engaged in Biomarker and Precision Medicine-centric drug development. Features:Detailed insights into biomarker discovery validation and diagnostic development with implementation strategiesLessons-learned from successful Precision Medicine case studiesA variety of exciting and emerging biomarker technologiesThe next frontiers and future challenges of biomarkers in Precision MedicineClaudio Carini Mark Fidock and Alain van Gool are internationally recognized as scientific leaders in Biomarkers and Precision Medicine. They have worked for decades in academia and pharmaceutical industry in EU USA and Asia. Currently Dr. Carini is Honorary Faculty at Kings’s College School of Medicine London UK. Dr. Fidock is Vice President of Precision Medicine Laboratories at AstraZeneca Cambridge UK. Prof. dr. van Gool is Head Translational Metabolic Laboratory at Radboud university medical school Nijmegen NL.

GBP 66.99
1

Time Series for Data Science Analysis and Forecasting

Time Series for Data Science Analysis and Forecasting

Data Science students and practitioners want to find a forecast that “works” and don’t want to be constrained to a single forecasting strategy Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble modelling for combining information from several strategies. Covering time series regression models exponential smoothing Holt-Winters forecasting and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject. This book is an accessible guide that doesn’t require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed. Features: Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing Holt Winters ARMA and ARIMA deep learning models including RNNs LSTMs GRUs and ensemble models composed of combinations of these models. Introduces the factor table representation of ARMA and ARIMA models. This representation is not available in any other book at this level and is extremely useful in both practice and pedagogy. Uses real world examples that can be readily found via web links from sources such as the US Bureau of Statistics Department of Transportation and the World Bank. There is an accompanying R package that is easy to use and requires little or no previous R experience. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use. | Time Series for Data Science Analysis and Forecasting

GBP 99.99
1

Handbook of Infectious Disease Data Analysis

Handbook of Infectious Disease Data Analysis

Recent years have seen an explosion in new kinds of data on infectious diseases including data on social contacts whole genome sequences of pathogens biomarkers for susceptibility to infection serological panel data and surveillance data. The Handbook of Infectious Disease Data Analysis provides an overview of many key statistical methods that have been developed in response to such new data streams and the associated ability to address key scientific and epidemiological questions. A unique feature of the Handbook is the wide range of topics covered. Key featuresContributors include many leading researchers in the fieldDivided into four main sections: Basic concepts Analysis of Outbreak Data Analysis of Seroprevalence Data Analysis of Surveillance DataNumerous case studies and examples throughoutProvides both introductory material and key reference material

GBP 56.99
1

Modelling Survival Data in Medical Research

Modelling Survival Data in Medical Research

Modelling Survival Data in Medical Research Fourth Edition describes the analysis of survival data illustrated using a wide range of examples from biomedical research. Written in a non-technical style it concentrates on how the techniques are used in practice. Starting with standard methods for summarising survival data Cox regression and parametric modelling the book covers many more advanced techniques including interval-censoring frailty modelling competing risks analysis of multiple events and dependent censoring. This new edition contains chapters on Bayesian survival analysis and use of the R software. Earlier chapters have been extensively revised and expanded to add new material on several topics. These include methods for assessing the predictive ability of a model joint models for longitudinal and survival data and modern methods for the analysis of interval-censored survival data. Features: Presents an accessible account of a wide range of statistical methods for analysing survival data Contains practical guidance on modelling survival data from the author’s many years of experience in teaching and consultancy Shows how Bayesian methods can be used to analyse survival data Includes details on how R can be used to carry out all the methods described with guidance on the interpretation of the resulting output Contains many real data examples and additional data sets that can be used for coursework All data sets used are available in electronic format from the publisher’s website Modelling Survival Data in Medical Research Fourth Edition is an invaluable resource for statisticians in the pharmaceutical industry and biomedical research centres research scientists and clinicians who are analysing their own data and students following undergraduate or postgraduate courses in survival analysis.

GBP 74.99
1

New Centrality Measures in Networks How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes

New Centrality Measures in Networks How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes

Over the last number of years there has been a growing interest in the analysis of complex networks which describe a wide range of real-world systems in nature and society. Identification of the central elements in such networks is one of the key research areas. Solutions to this problem are important for making strategic decisions and studying the behavior of dynamic processes e. g. epidemic spread. The importance of nodes has been studied using various centrality measures. Generally it should be considered that most real systems are not homogeneous: nodes may have individual attributes and influence each other in groups while connections between nodes may describe different types of relations. Thus critical nodes detection is not a straightforward process. New Centrality Measures in Networks presents a class of new centrality measures which take into account individual attributes of nodes the possibility of group influence and long-range interactions and discusses all their new features. The book provides a wide range of applications of network analysis in several fields – financial networks international migration global trade global food network arms transfers networks of terrorist groups and networks of international journals in economics. Real-world studies of networks indicate that the proposed centrality measures can identify important nodes in different applications. Starting from the basic ideas the development of the indices and their advantages compared to existing centrality measures are presented. Features Built around real-world case studies in a variety of different areas (finance migration trade etc. ) Suitable for students and professional researchers with an interest in complex network analysis Paired with a software package for readers who wish to apply the proposed models of centrality (in Python) available at https://github. com/SergSHV/slric. | New Centrality Measures in Networks How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes

GBP 48.99
1

Handbook of Survival Analysis

Handbook of Survival Analysis

Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time. With chapters written by leading researchers in the field the handbook focuses on advances in survival analysis techniques covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers the book provides: An introduction to various areas in survival analysis for graduate students and novices A reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysis A useful guide to statistical methods for analyzing survival data experiments for practicing statisticians

GBP 66.99
1

Omic Association Studies with R and Bioconductor

Omic Association Studies with R and Bioconductor

After the great expansion of genome-wide association studies their scientific methodology and notably their data analysis has matured in recent years and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear readable programming codes for readers to reproduce and adapt to their own data. Emphasises extracting biologically meaningful associations between traits of interest and genomic transcriptomic and epigenomic data Uses up-to-date methods to exploit omic data Presents methods through specific examples and computing sessionsSupplemented by a website including code datasets and solutions | Omic Association Studies with R and Bioconductor

GBP 44.99
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An Introduction to Numerical Methods A MATLAB Approach

An Introduction to Numerical Methods A MATLAB Approach

An Introduction to Numerical Methods: A MATLAB® Approach Fifth Edition continues to offer readers an accessible and practical introduction to numerical analysis. It presents a wide range of useful and important algorithms for scientific and engineering applications using MATLAB to illustrate each numerical method with full details of the computed results so that the main steps are easily visualized and interpreted. This edition also includes new chapters on Approximation of Continuous Functions and Dealing with Large Sets of Data. Features: Covers the most common numerical methods encountered in science and engineering Illustrates the methods using MATLAB Ideal as an undergraduate textbook for numerical analysis Presents numerous examples and exercises with selected answers provided at the back of the book Accompanied by downloadable MATLAB code hosted at https/www. routledge. com/9781032406824 | An Introduction to Numerical Methods A MATLAB® Approach

GBP 52.99
1

Advanced Studies in Multi-Criteria Decision Making

Advanced Studies in Multi-Criteria Decision Making

With contributions from some of the top academics and scientists in the field Advanced Studies in Multi-Criteria Decision Making presents an updated view of the landscape of Decision Sciences current research topics the interaction with other sciences and fields as well as the prospects and challenges at an international level. Given that Decision Sciences are recognized today as indispensable for confronting the major societal challenges in science and technology this book would be of interest to decision-makers managers and researchers from academia and industrial/services companies that would like a fresh insight into MCDM. FeaturesIntegrates a wide range of scientific fields with a general reader approach including applied researchers from the social business enterprise sciencesSuitable for academics and professionalsPresents a broad coverage of MCDM tools either in industry or in services companies and systemsProvides a fresh overview on MCDM studies promoted by prestigious R&D institutions

GBP 44.99
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Multiplicative Partial Differential Equations

Multiplicative Partial Differential Equations

Multiplicative Partial Differential Equations presents an introduction to the theory of multiplicative partial differential equations (MPDEs). It is suitable for all types of basic courses on MPDEs. The authors' aim is to present a clear and well-organized treatment of the concepts behind the development of mathematics and solution techniques. The text is presented in a highly readable mathematically solid format. Many practical problems are illustrated displaying a wide variety of solution techniques. Features Includes new classification and canonical forms of second-order MPDEs Proposes the latest techniques in solving the multiplicative wave equation such as the method of separation of variables and the energy method Useful in allowing for the basic properties of multiplicative elliptic problems fundamental solutions multiplicative integral representation of multiplicative harmonic functions meant-value formulas strong principle of maximum multiplicative Poisson equation multiplicative Green functions method of separation of variables and theorems of Liouville and Harnack

GBP 99.99
1