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Sample Size Calculations in Clinical Research

Sample Size Calculations in Clinical Research

Praise for the Second Edition:… this is a useful comprehensive compendium of almost every possible sample size formula. The strong organization and carefully defined formulae will aid any researcher designing a study. BiometricsThis impressive book contains formulae for computing sample size in a wide range of settings. One-sample studies and two-sample comparisons for quantitative binary and time-to-event outcomes are covered comprehensively with separate sample size formulae for testing equality non-inferiority and equivalence. Many less familiar topics are also covered … – Journal of the Royal Statistical SocietySample Size Calculations in Clinical Research Third Edition presents statistical procedures for performing sample size calculations during various phases of clinical research and development. A comprehensive and unified presentation of statistical concepts and practical applications this book includes a well-balanced summary of current and emerging clinical issues regulatory requirements and recently developed statistical methodologies for sample size calculation. Features:Compares the relative merits and disadvantages of statistical methods for sample size calculationsExplains how the formulae and procedures for sample size calculations can be used in a variety of clinical research and development stagesPresents real-world examples from several therapeutic areas including cardiovascular medicine the central nervous system anti-infective medicine oncology and women’s healthProvides sample size calculations for dose response studies microarray studies and Bayesian approachesThis new edition is updated throughout includes many new sections and five new chapters on emerging topics: two stage seamless adaptive designs cluster randomized trial design zero-inflated Poisson distribution clinical trials with extremely low incidence rates and clinical trial simulation.

GBP 38.99
1

Modelling Spatial and Spatial-Temporal Data A Bayesian Approach

Modelling Spatial and Spatial-Temporal Data A Bayesian Approach

Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is aimed at statisticians and quantitative social economic and public health students and researchers who work with small-area spatial and spatial-temporal data. It assumes a grounding in statistical theory up to the standard linear regression model. The book compares both hierarchical and spatial econometric modelling providing both a reference and a teaching text with exercises in each chapter. The book provides a fully Bayesian self-contained treatment of the underlying statistical theory with chapters dedicated to substantive applications. The book includes WinBUGS code and R code and all datasets are available online. Part I covers fundamental issues arising when modelling spatial and spatial-temporal data. Part II focuses on modelling cross-sectional spatial data and begins by describing exploratory methods that help guide the modelling process. There are then two theoretical chapters on Bayesian models and a chapter of applications. Two chapters follow on spatial econometric modelling one describing different models the other substantive applications. Part III discusses modelling spatial-temporal data first introducing models for time series data. Exploratory methods for detecting different types of space-time interaction are presented followed by two chapters on the theory of space-time separable (without space-time interaction) and inseparable (with space-time interaction) models. An applications chapter includes: the evaluation of a policy intervention; analysing the temporal dynamics of crime hotspots; chronic disease surveillance; and testing for evidence of spatial spillovers in the spread of an infectious disease. A final chapter suggests some future directions and challenges. Robert Haining is Emeritus Professor in Human Geography University of Cambridge England. He is the author of Spatial Data Analysis in the Social and Environmental Sciences (1990) and Spatial Data Analysis: Theory and Practice (2003). He is a Fellow of the RGS-IBG and of the Academy of Social Sciences. Guangquan Li is Senior Lecturer in Statistics in the Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle England. His research includes the development and application of Bayesian methods in the social and health sciences. He is a Fellow of the Royal Statistical Society. | Modelling Spatial and Spatial-Temporal Data A Bayesian Approach

GBP 44.99
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Linear Models with Python

Linear Models with Python

Praise for Linear Models with R: This book is a must-have tool for anyone interested in understanding and applying linear models. The logical ordering of the chapters is well thought out and portrays Faraway’s wealth of experience in teaching and using linear models. … It lays down the material in a logical and intricate manner and makes linear modeling appealing to researchers from virtually all fields of study. Biometrical Journal Throughout it gives plenty of insight … with comments that even the seasoned practitioner will appreciate. Interspersed with R code and the output that it produces one can find many little gems of what I think is sound statistical advice well epitomized with the examples chosen…I read it with delight and think that the same will be true with anyone who is engaged in the use or teaching of linear models. Journal of the Royal Statistical Society Like its widely praised best-selling companion version Linear Models with R this book replaces R with Python to seamlessly give a coherent exposition of the practice of linear modeling. Linear Models with Python offers up-to-date insight on essential data analysis topics from estimation inference and prediction to missing data factorial models and block designs. Numerous examples illustrate how to apply the different methods using Python. Features: Python is a powerful open source programming language increasingly being used in data science machine learning and computer science. Python and R are similar but R was designed for statistics while Python is multi-talented. This version replaces R with Python to make it accessible to a greater number of users outside of statistics including those from Machine Learning. A reader coming to this book from an ML background will learn new statistical perspectives on learning from data. Topics include Model Selection Shrinkage Experiments with Blocks and Missing Data. Includes an Appendix on Python for beginners. Linear Models with Python explains how to use linear models in physical science engineering social science and business applications. It is ideal as a textbook for linear models or linear regression courses.

GBP 82.99
1

Statistics in Engineering With Examples in MATLAB and R Second Edition

Statistics in Engineering With Examples in MATLAB and R Second Edition

Engineers are expected to design structures and machines that can operate in challenging and volatile environments while allowing for variation in materials and noise in measurements and signals. Statistics in Engineering Second Edition: With Examples in MATLAB and R covers the fundamentals of probability and statistics and explains how to use these basic techniques to estimate and model random variation in the context of engineering analysis and design in all types of environments. The first eight chapters cover probability and probability distributions graphical displays of data and descriptive statistics combinations of random variables and propagation of error statistical inference bivariate distributions and correlation linear regression on a single predictor variable and the measurement error model. This leads to chapters including multiple regression; comparisons of several means and split-plot designs together with analysis of variance; probability models; and sampling strategies. Distinctive features include: All examples based on work in industry consulting to industry and research for industry Examples and case studies include all engineering disciplinesEmphasis on probabilistic modeling including decision trees Markov chains and processes and structure functionsIntuitive explanations are followed by succinct mathematical justificationsEmphasis on random number generation that is used for stochastic simulations of engineering systems demonstration of key concepts and implementation of bootstrap methods for inferenceUse of MATLAB and the open source software R both of which have an extensive range of statistical functions for standard analyses and also enable programing of specific applicationsUse of multiple regression for times series models and analysis of factorial and central composite designs Inclusion of topics such as Weibull analysis of failure times and split-plot designs that are commonly used in industry but are not usually included in introductory textbooksExperiments designed to show fundamental concepts that have been tested with large classes working in small groupsWebsite with additional materials that is regularly updatedAndrew Metcalfe David Green Andrew Smith and Jonathan Tuke have taught probability and statistics to students of engineering at the University of Adelaide for many years and have substantial industry experience. Their current research includes applications to water resources engineering mining and telecommunications. Mahayaudin Mansor worked in banking and insurance before teaching statistics and business mathematics at the Universiti Tun Abdul Razak Malaysia and is currently a researcher specializing in data analytics and quantitative research in the Health Economics and Social Policy Research Group at the Australian Centre for Precision Health University of South Australia. Tony Greenfield formerly Head of Process Computing and Statistics at the British Iron and Steel Research Association is a statistical consultant. He has been awarded the Chambers Medal for outstanding services to the Royal Statistical Society; the George Box Medal by the European Network for Business and Industrial Statistics for Outstanding Contributions to Industrial Statistics; and the William G. Hunter Award by the American Society for | Statistics in Engineering With Examples in MATLAB® and R Second Edition

GBP 44.99
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