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Hybrid Frequentist/Bayesian Power and Bayesian Power in Planning Clinical Trials

Hybrid Frequentist/Bayesian Power and Bayesian Power in Planning Clinical Trials

Hybrid Frequentist/Bayesian Power and Bayesian Power in Planning Clinical Trials provides a practical introduction to unconditional approaches to planning randomised clinical trials particularly aimed at drug development in the pharmaceutical industry. This book is aimed at providing guidance to practitioners in using average power assurance and related concepts. This book brings together recent research and sets them in a consistent framework and provides a fresh insight into how such methods can be used. Features: A focus on normal theory linking average power expected power predictive power assurance conditional Bayesian power and Bayesian power. Extensions of the concepts to binomial and time-to-event outcomes and non-inferiority trials An investigation into the upper bound on average power assurance and Bayesian power based on the prior probability of a positive treatment effect Application of assurance to a series of trials in a development program and an introduction of the assurance of an individual trial conditional on the positive outcome of an earlier trial in the program or to the successful outcome of an interim analysis Prior distribution of power and sample size Extension of the basic approach to proof-of-concept trials with dual success criteria Investigation of the connection between conditional and predictive power at an interim analysis and power and assurance Introduction of the idea of surety in sample sizing of clinical trials based on the width of the confidence intervals for the treatment effect and an unconditional version.

GBP 99.99
1

Statistical Simulation Power Method Polynomials and Other Transformations

Statistical Simulation Power Method Polynomials and Other Transformations

Although power method polynomials based on the standard normal distributions have been used in many different contexts for the past 30 years it was not until recently that the probability density function (pdf) and cumulative distribution function (cdf) were derived and made available. Focusing on both univariate and multivariate nonnormal data generation Statistical Simulation: Power Method Polynomials and Other Transformations presents techniques for conducting a Monte Carlo simulation study. It shows how to use power method polynomials for simulating univariate and multivariate nonnormal distributions with specified cumulants and correlation matrices. The book first explores the methodology underlying the power method before demonstrating this method through examples of standard normal logistic and uniform power method pdfs. It also discusses methods for improving the performance of a simulation based on power method polynomials. The book then develops simulation procedures for systems of linear statistical models intraclass correlation coefficients and correlated continuous variates and ranks. Numerical examples and results from Monte Carlo simulations illustrate these procedures. The final chapter describes how the g-and-h and generalized lambda distribution (GLD) transformations are special applications of the more general multivariate nonnormal data generation approach. Throughout the text the author employs Mathematica® in a range of procedures and offers the source code for download online. Written by a longtime researcher of the power method this book explains how to simulate nonnormal distributions via easy-to-use power method polynomials. By using the methodology and techniques developed in the text readers can evaluate different transformations in terms of comparing percentiles measures of central tendency goodness-of-fit tests and more. | Statistical Simulation Power Method Polynomials and Other Transformations

GBP 64.99
1

Real World AI Ethics for Data Scientists Practical Case Studies

How Things Work The Computer Science Edition

Data Science for Mathematicians

Embedded and Networking Systems Design Software and Implementation

Embedded and Networking Systems Design Software and Implementation

Embedded and Networking Systems: Design Software and Implementation explores issues related to the design and synthesis of high-performance embedded computer systems and networks. The emphasis is on the fundamental concepts and analytical techniques that are applicable to a range of embedded and networking applications rather than on specific embedded architectures software development or system-level integration. This system point of view guides designers in dealing with the trade-offs to optimize performance power cost and other system-level non-functional requirements. The book brings together contributions by researchers and experts from around the world offering a global view of the latest research and development in embedded and networking systems. Chapters highlight the evolution and trends in the field and supply a fundamental and analytical understanding of some underlying technologies. Topics include the co-design of embedded systems code optimization for a variety of applications power and performance trade-offs benchmarks for evaluating embedded systems and their components and mobile sensor network systems. The book also looks at novel applications such as mobile sensor systems and video networks. A comprehensive review of groundbreaking technology and applications this book is a timely resource for system designers researchers and students interested in the possibilities of embedded and networking systems. It gives readers a better understanding of an emerging technology evolution that is helping drive telecommunications into the next decade. | Embedded and Networking Systems Design Software and Implementation

GBP 77.99
1

Handbook of Statistical Methods for Case-Control Studies

From Computing to Computational Thinking

Pocket Book of Integrals and Mathematical Formulas

Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers

Advanced Wireless Communication and Sensor Networks Applications and Simulations

Applied Computer Vision and Soft Computing with Interpretable AI

The Effect An Introduction to Research Design and Causality

Regression Modeling Methods Theory and Computation with SAS

Data Science for Wind Energy

LabVIEW A Developer's Guide to Real World Integration

Signal Processing A Mathematical Approach Second Edition

Signal Processing A Mathematical Approach Second Edition

Signal Processing: A Mathematical Approach is designed to show how many of the mathematical tools the reader knows can be used to understand and employ signal processing techniques in an applied environment. Assuming an advanced undergraduate- or graduate-level understanding of mathematics—including familiarity with Fourier series matrices probability and statistics—this Second Edition: Contains new chapters on convolution and the vector DFT plane-wave propagation and the BLUE and Kalman filtersExpands the material on Fourier analysis to three new chapters to provide additional background informationPresents real-world examples of applications that demonstrate how mathematics is used in remote sensingFeaturing problems for use in the classroom or practice Signal Processing: A Mathematical Approach Second Edition covers topics such as Fourier series and transforms in one and several variables; applications to acoustic and electro-magnetic propagation models transmission and emission tomography and image reconstruction; sampling and the limited data problem; matrix methods singular value decomposition and data compression; optimization techniques in signal and image reconstruction from projections; autocorrelations and power spectra; high-resolution methods; detection and optimal filtering; and eigenvector-based methods for array processing and statistical filtering time-frequency analysis and wavelets. | Signal Processing A Mathematical Approach Second Edition

GBP 44.99
1

Intelligent Systems in Healthcare and Disease Identification using Data Science

Intelligent Systems in Healthcare and Disease Identification using Data Science

The health technology has become a hot topic in academic research. It employs the theory of social networks into the different levels of the prediction and analysis and has brought new possibilities for the development of technology. This book is a descriptive summary of challenges and methods using disease identification with various case studies from diverse authors across the globe. One of the new buzzwords in healthcare sector that has become popular over years is health informatics. Healthcare professionals must deal with an increasing number of computers and computer programs in their daily work. With rapid growth of digital data the role of analytics in healthcare has created a significant impact on healthcare professional’s life. Improvements in storage data computational power and paral- lelization has also contributed to uptake this technology. This book is intended for use by researchers health informatics professionals academicians and undergraduate and postgraduate students interested in knowing more about health informatics. It aims to provide a brief overview about informatics its history and area of practice laws in health informatics challenges and technologies in health informatics applica- tion of informatics in various sectors and so on. Finally the research avenues in health informatics along with some case studies are discussed. | Intelligent Systems in Healthcare and Disease Identification using Data Science

GBP 84.99
1

Innovative Methods for Rare Disease Drug Development

Innovative Methods for Rare Disease Drug Development

In the United States a rare disease is defined by the Orphan Drug Act as a disorder or condition that affects fewer than 200 000 persons. For the approval of orphan drug products for rare diseases the traditional approach of power analysis for sample size calculation is not feasible because there are only limited number of subjects available for clinical trials. In this case innovative approaches are needed for providing substantial evidence meeting the same standards for statistical assurance as drugs used to treat common conditions. Innovative Methods for Rare Disease Drug Development focuses on biostatistical applications in terms of design and analysis in pharmaceutical research and development from both regulatory and scientific (statistical) perspectives. Key Features: Reviews critical issues (e. g. endpoint/margin selection sample size requirements and complex innovative design). Provides better understanding of statistical concepts and methods which may be used in regulatory review and approval. Clarifies controversial statistical issues in regulatory review and approval accurately and reliably. Makes recommendations to evaluate rare diseases regulatory submissions. Proposes innovative study designs and statistical methods for rare diseases drug development including n-of-1 trial design adaptive trial design and master protocols like platform trials. Provides insight regarding current regulatory guidance on rare diseases drug development like gene therapy.

GBP 44.99
1

A Concise Introduction to Pure Mathematics

Approximate Analytical Methods for Solving Ordinary Differential Equations

Approximate Analytical Methods for Solving Ordinary Differential Equations

Approximate Analytical Methods for Solving Ordinary Differential Equations (ODEs) is the first book to present all of the available approximate methods for solving ODEs eliminating the need to wade through multiple books and articles. It covers both well-established techniques and recently developed procedures including the classical series solution method diverse perturbation methods pioneering asymptotic methods and the latest homotopy methods. The book is suitable not only for mathematicians and engineers but also for biologists physicists and economists. It gives a complete description of the methods without going deep into rigorous mathematical aspects. Detailed examples illustrate the application of the methods to solve real-world problems. The authors introduce the classical power series method for solving differential equations before moving on to asymptotic methods. They next show how perturbation methods are used to understand physical phenomena whose mathematical formulation involves a perturbation parameter and explain how the multiple-scale technique solves problems whose solution cannot be completely described on a single timescale. They then describe the Wentzel Kramers and Brillown (WKB) method that helps solve both problems that oscillate rapidly and problems that have a sudden change in the behavior of the solution function at a point in the interval. The book concludes with recent nonperturbation methods that provide solutions to a much wider class of problems and recent analytical methods based on the concept of homotopy of topology.

GBP 59.99
1

Probability Statistics and Data A Fresh Approach Using R

Probability Statistics and Data A Fresh Approach Using R

This book is a fresh approach to a calculus based first course in probability and statistics using R throughout to give a central role to data and simulation. The book introduces probability with Monte Carlo simulation as an essential tool. Simulation makes challenging probability questions quickly accessible and easily understandable. Mathematical approaches are included using calculus when appropriate but are always connected to experimental computations. Using R and simulation gives a nuanced understanding of statistical inference. The impact of departure from assumptions in statistical tests is emphasized quantified using simulations and demonstrated with real data. The book compares parametric and non-parametric methods through simulation allowing for a thorough investigation of testing error and power. The text builds R skills from the outset allowing modern methods of resampling and cross validation to be introduced along with traditional statistical techniques. Fifty-two data sets are included in the complementary R package fosdata. Most of these data sets are from recently published papers so that you are working with current real data which is often large and messy. Two central chapters use powerful tidyverse tools (dplyr ggplot2 tidyr stringr) to wrangle data and produce meaningful visualizations. Preliminary versions of the book have been used for five semesters at Saint Louis University and the majority of the more than 400 exercises have been classroom tested. | Probability Statistics and Data A Fresh Approach Using R

GBP 82.99
1

Entropy Randomization in Machine Learning

Entropy Randomization in Machine Learning

Entropy Randomization in Machine Learning presents a new approach to machine learning—entropy randomization—to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). Randomized machine-learning procedures involve models with random parameters and maximum entropy estimates of the probability density functions of the model parameters under balance conditions with measured data. Optimality conditions are derived in the form of nonlinear equations with integral components. A new numerical random search method is developed for solving these equations in a probabilistic sense. Along with the theoretical foundations of randomized machine learning Entropy Randomization in Machine Learning considers several applications to binary classification modelling the dynamics of the Earth’s population predicting seasonal electric load fluctuations of power supply systems and forecasting the thermokarst lakes area in Western Siberia. Features • A systematic presentation of the randomized machine-learning problem: from data processing through structuring randomized models and algorithmic procedure to the solution of applications-relevant problems in different fields • Provides new numerical methods for random global optimization and computation of multidimensional integrals • A universal algorithm for randomized machine learning This book will appeal to undergraduates and postgraduates specializing in artificial intelligence and machine learning researchers and engineers involved in the development of applied machine learning systems and researchers of forecasting problems in various fields.

GBP 82.99
1

Basic Statistical Methods and Models for the Sciences

Basic Statistical Methods and Models for the Sciences

The use of statistics in biology medicine engineering and the sciences has grown dramatically in recent years and having a basic background in the subject has become a near necessity for students and researchers in these fields. Although many introductory statistics books already exist too often their focus leans towards theory and few help readers gain effective experience in using a standard statistical software package. Designed to be used in a first course for graduate or upper-level undergraduate students Basic Statistical Methods and Models builds a practical foundation in the use of statistical tools and imparts a clear understanding of their underlying assumptions and limitations. Without getting bogged down in proofs and derivations thorough discussions help readers understand why the stated methods and results are reasonable. The use of the statistical software Minitab is integrated throughout the book giving readers valuable experience with computer simulation and problem-solving techniques. The author focuses on applications and the models appropriate to each problem while emphasizing Monte Carlo methods the Central Limit Theorem confidence intervals and power functions. The text assumes that readers have some degree of maturity in mathematics but it does not require the use of calculus. This along with its very clear explanations generous number of exercises and demonstrations of the extensive uses of statistics in diverse areas applications make Basic Statistical Methods and Models highly accessible to students in a wide range of disciplines. | Basic Statistical Methods and Models for the Sciences

GBP 59.99
1

Statistical Design Monitoring and Analysis of Clinical Trials Principles and Methods

Statistical Design Monitoring and Analysis of Clinical Trials Principles and Methods

Statistical Design Monitoring and Analysis of Clinical Trials Second Edition concentrates on the biostatistics component of clinical trials. This new edition is updated throughout and includes five new chapters. Developed from the authors’ courses taught to public health and medical students residents and fellows during the past 20 years the text shows how biostatistics in clinical trials is an integration of many fundamental scientific principles and statistical methods. The book begins with ethical and safety principles core trial design concepts the principles and methods of sample size and power calculation and analysis of covariance and stratified analysis. It then focuses on sequential designs and methods for two-stage Phase II cancer trials to Phase III group sequential trials covering monitoring safety futility and efficacy. The authors also discuss the development of sample size reestimation and adaptive group sequential procedures phase 2/3 seamless design and trials with predictive biomarkers exploit multiple testing procedures and explain the concept of estimand intercurrent events and different missing data processes and describe how to analyze incomplete data by proper multiple imputations. This text reflects the academic research commercial development and public health aspects of clinical trials. It gives students and practitioners a multidisciplinary understanding of the concepts and techniques involved in designing monitoring and analyzing various types of trials. The book’s balanced set of homework assignments and in-class exercises are appropriate for students and researchers in (bio)statistics epidemiology medicine pharmacy and public health. | Statistical Design Monitoring and Analysis of Clinical Trials Principles and Methods

GBP 82.99
1