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Advanced Survival Models

Advanced Survival Models

Survival data analysis is a very broad field of statistics encompassing a large variety of methods used in a wide range of applications and in particular in medical research. During the last twenty years several extensions of classical survival models have been developed to address particular situations often encountered in practice. This book aims to gather in a single reference the most commonly used extensions such as frailty models (in case of unobserved heterogeneity or clustered data) cure models (when a fraction of the population will not experience the event of interest) competing risk models (in case of different types of event) and joint survival models for a time-to-event endpoint and a longitudinal outcome. Features Presents state-of-the art approaches for different advanced survival models including frailty models cure models competing risk models and joint models for a longitudinal and a survival outcome Uses consistent notation throughout the book for the different techniques presented Explains in which situation each of these models should be used and how they are linked to specific research questions Focuses on the understanding of the models their implementation and their interpretation with an appropriate level of methodological development for masters students and applied statisticians Provides references to existing R packages and SAS procedure or macros and illustrates the use of the main ones on real datasets This book is primarily aimed at applied statisticians and graduate students of statistics and biostatistics. It can also serve as an introductory reference for methodological researchers interested in the main extensions of classical survival analysis.

GBP 42.99
1

Statistical Models in Toxicology

Statistical Models in Toxicology

Statistical Models in Toxicology presents an up-to-date and comprehensive account of statistical theory topics that occur in toxicology. The attention given by statisticians to the problem of health risk estimation for environmental and occupational exposures in the last few decades has created excitement and optimism among both statisticians and toxicologists. The development of modern statistical techniques with solid mathematical foundations in the twentieth century and the advent of modern computers in the latter part of the century gave way to the development of many statistical models and methods to describe toxicological processes and attempts to solve the associated problems. Not only have the models enjoyed a high level of elegance and sophistication mathematically but they are widely used by industry and government regulatory agencies. Features:Focuses on describing the statistical models in environmental toxicology that facilitate the assessment of risk mainly in humans. The properties and shortfalls of each model are discussed and its impact in the process of risk assessment is examined. Discusses models that assess the risk of mixtures of chemicals. Presents statistical models that are developed for risk estimation in different aspects of environmental toxicology including cancer and carcinogenic substances. Includes models for developmental and reproductive toxicity risk assessment risk assessment in continuous outcomes and developmental neurotoxicity. Contains numerous examples and exercises. Statistical Models in Toxicology introduces a wide variety of statistical models that are currently utilized for dose-response modeling and risk analysis. These models are often developed based on design and regulatory guidelines of toxicological experiments. The book is suitable for practitioners or it can be used as a textbook for advanced undergraduate or graduate students of mathematics and statistics.

GBP 44.99
1

Mixed Effects Models for Complex Data

Mixed Effects Models for Complex Data

Although standard mixed effects models are useful in a range of studies other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts missing data measurement errors censoring and outliers. For each class of mixed effects model the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data the book introduces linear mixed effects (LME) models generalized linear mixed models (GLMMs) nonlinear mixed effects (NLME) models and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values measurement errors censoring and outliers. Self-contained coverage of specific topicsSubsequent chapters delve more deeply into missing data problems covariate measurement errors and censored responses in mixed effects models. Focusing on incomplete data the book also covers survival and frailty models joint models of survival and longitudinal data robust methods for mixed effects models marginal generalized estimating equation (GEE) models for longitudinal or clustered data and Bayesian methods for mixed effects models. Background materialIn the appendix the author provides background information such as likelihood theory the Gibbs sampler rejection and importance sampling methods numerical integration methods optimization methods bootstrap and matrix algebra. Failure to properly address missing data measurement errors and other issues in statistical analyses can lead

GBP 59.99
1

Diffusion Models of Environmental Transport

Diffusion Models of Environmental Transport

Fate and transport models are critical components in the determination of the exposure to and risk from hazardous contaminants. Analytical models are preferable because they are generally more accessible more reliable and require fewer computational resources. Surprisingly until today only a limited number of analytical models have been accessible in the literature. Now there is Diffusion Models of Environmental Transport which provides more than 40 analytical models of diffusion and advective-diffusion in one two and three layer systems subject to a wide range of boundary and initial conditions. This text illustrates applications to contaminant transport in sediments and soils including porewater and vapor transport and also provides Mathcad spreadsheets to aid in the use of these models. The authors supply complete details of the solutions to the models for those who wish for a deeper understanding. For others who do not have the time or the need the solutions themselves are ready to be picked up and used. Reible and Choy use their 20-plus years of cumulative experience to create a thorough exploration of fate and transport models. This comprehensive text furnishes an invaluable reference for students and environmental professionals.

GBP 59.99
1

Mixed Effects Models for the Population Approach Models Tasks Methods and Tools

Mixed Effects Models for the Population Approach Models Tasks Methods and Tools

Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects ModelsMixed Effects Models for the Population Approach: Models Tasks Methods and Tools presents a rigorous framework for describing implementing and using mixed effects models. With these models readers can perform parameter estimation and modeling across a whole population of individuals at the same time. Easy-to-Use Techniques and Tools for Real-World Data ModelingThe book first shows how the framework allows model representation for different data types including continuous categorical count and time-to-event data. This leads to the use of generic methods such as the stochastic approximation of the EM algorithm (SAEM) for modeling these diverse data types. The book also covers other essential methods including Markov chain Monte Carlo (MCMC) and importance sampling techniques. The author uses publicly available software tools to illustrate modeling tasks. Methods are implemented in Monolix and models are visually explored using Mlxplore and simulated using Simulx. Careful Balance of Mathematical Representation and Practical ImplementationThis book takes readers through the whole modeling process from defining/creating a parametric model to performing tasks on the model using various mathematical methods. Statisticians and mathematicians will appreciate the rigorous representation of the models and theoretical properties of the methods while modelers will welcome the practical capabilities of the tools. The book is also useful for training and teaching in any field where population modeling occurs. | Mixed Effects Models for the Population Approach Models Tasks Methods and Tools

GBP 42.99
1

Logistic Regression Models

Logistic Regression Models

Logistic Regression Models presents an overview of the full range of logistic models including binary proportional ordered partially ordered and unordered categorical response regression procedures. Other topics discussed include panel survey skewed penalized and exact logistic models. The text illustrates how to apply the various models to health environmental physical and social science data. Examples illustrate successful modelingThe text first provides basic terminology and concepts before explaining the foremost methods of estimation (maximum likelihood and IRLS) appropriate for logistic models. It then presents an in-depth discussion of related terminology and examines logistic regression model development and interpretation of the results. After focusing on the construction and interpretation of various interactions the author evaluates assumptions and goodness-of-fit tests that can be used for model assessment. He also covers binomial logistic regression varieties of overdispersion and a number of extensions to the basic binary and binomial logistic model. Both real and simulated data are used to explain and test the concepts involved. The appendices give an overview of marginal effects and discrete change as well as a 30-page tutorial on using Stata commands related to the examples used in the text. Stata is used for most examples while R is provided at the end of the chapters to replicate examples in the text. Apply the models to your own dataData files for examples and questions used in the text as well as code for user-authored commands are provided on the book’s website formatted in Stata R Excel SAS SPSS and Limdep. See Professor Hilbe discuss the book.

GBP 52.99
1

A Primer on Linear Models

A Primer on Linear Models

A Primer on Linear Models presents a unified thorough and rigorous development of the theory behind the statistical methodology of regression and analysis of variance (ANOVA). It seamlessly incorporates these concepts using non-full-rank design matrices and emphasizes the exact finite sample theory supporting common statistical methods. With coverage steadily progressing in complexity the text first provides examples of the general linear model including multiple regression models one-way ANOVA mixed-effects models and time series models. It then introduces the basic algebra and geometry of the linear least squares problem before delving into estimability and the Gauss Markov model. After presenting the statistical tools of hypothesis tests and confidence intervals the author analyzes mixed models such as two-way mixed ANOVA and the multivariate linear model. The appendices review linear algebra fundamentals and results as well as Lagrange multipliers. This book enables complete comprehension of the material by taking a general unifying approach to the theory fundamentals and exact results of linear models.

GBP 175.00
1

Cure Models Methods Applications and Implementation

Cure Models Methods Applications and Implementation

Cure Models: Methods Applications and Implementation is the first book in the last 25 years that provides a comprehensive and systematic introduction to the basics of modern cure models including estimation inference and software. This book is useful for statistical researchers and graduate students and practitioners in other disciplines to have a thorough review of modern cure model methodology and to seek appropriate cure models in applications. The prerequisites of this book include some basic knowledge of statistical modeling survival models and R and SAS for data analysis. The book features real-world examples from clinical trials and population-based studies and a detailed introduction to R packages SAS macros and WinBUGS programs to fit some cure models. The main topics covered include the foundation of statistical estimation and inference of cure models for independent and right-censored survival data cure modeling for multivariate recurrent-event and competing-risks survival data and joint modeling with longitudinal data statistical testing for the existence and difference of cure rates and sufficient follow-up new developments in Bayesian cure models applications of cure models in public health research and clinical trials. | Cure Models Methods Applications and Implementation

GBP 35.99
1

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

From Models to Simulations

From Models to Simulations

This book analyses the impact computerization has had on contemporary science and explains the origins technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism computation data acquisition data and visualization and how these factors have led to the spread of simulation models since the 1950s. Using historical comparative and interpretative case studies from a range of disciplines with a particular emphasis on the case of plant studies the author shows how and why computers data treatment devices and programming languages have occasioned a gradual but irresistible and massive shift from mathematical models to computer simulations. | From Models to Simulations

GBP 38.99
1

Accelerated Life Models Modeling and Statistical Analysis

Accelerated Life Models Modeling and Statistical Analysis

The authors of this monograph have developed a large and important class of survival analysis models that generalize most of the existing models. In a unified systematic presentation this monograph fully details those models and explores areas of accelerated life testing usually only touched upon in the literature. Accelerated Life Models: Modeling and Statistical Analysis presents models methods of data collection and statistical analysis for failure-time regression data in accelerated life testing and for degradation data with explanatory variables. In addition to the classical results the authors devote considerable attention to models with time-varying explanatory variables and to methods of semiparametric estimation. They also examine the simultaneous analysis of degradation and failure-time data when the intensities of failure in different modes depend on the level of degradation and the values of explanatory variables. The authors avoid technical details by explaining the ideas and referring to resources where thorough analysis can be found. Whether used for teaching research or general reference Accelerated Life Models: Modeling and Statistical Analysis provides new and known models and modern methods of accelerated life data analysis. | Accelerated Life Models Modeling and Statistical Analysis

GBP 59.99
1

The Procurement Models Handbook

The Procurement Models Handbook

Building from the previous two successful editions The Procurement Models Handbook is an essential resource for everyone working in the procurement profession including those selling directly to it. The authors provide the reader with a useful guide to the business models most frequently applied in the procurement and supply chain arena. Procurement and supply chain management are two of the highest contributors to corporate success in the modern world. This third edition is a new revised international version with additional tools that reflect the value of procurement in our globally-connected world. The authors have included over 50 well-established strategic and operational models that have a proven track record of delivering value over years of practice. Each model is presented pictorially with explanatory commentary on its practical application to support. These models are designed to save unnecessary cost and deliver significant benefits for their user and have been carefully selected by the authors based on their originality and usefulness for practical application in the context of procurement and the supply chain. The Procurement Models Handbook is an invaluable and enduring source of reference for practitioners and business managers as well as an essential learning support for business and procurement students.

GBP 31.99
1

Systems Evaluation Methods Models and Applications

Systems Evaluation Methods Models and Applications

A book in the Systems Evaluation Prediction and Decision-Making Series Systems Evaluation: Methods Models and Applications covers the evolutionary course of systems evaluation methods clearly and concisely. Outlining a wide range of methods and models it begins by examining the method of qualitative assessment. Next it describes the process and methods for building an index system of evaluation and considers the compared evaluation and the logical framework approach analytic hierarchy process (AHP) and the data envelopment analysis (DEA) relative efficiency evaluation method. Unique in its emphasis on the practical applications of systems evaluation methods and models the book introduces several new evaluation models of grey system including general grey incidence model grey incidence models based on similarity and closeness grey cluster evaluation based on triangular whitenization functions and multi-attribute grey target decision model. Explaining intricate concepts in language that is easy to understand it provides step-by-step explanations of the various methods and models. The text illustrates the practical application analysis and computation of systems evaluation methods and models with an abundance of practical examples and empirical studies. The case studies examine post evaluation of road-bridge construction projects the efficiency evaluation of the science and technology activities the evaluation of energy-saving projects in China and the evaluation and selection of international cooperation projects. | Systems Evaluation Methods Models and Applications

GBP 69.99
1

Models-based Practice in Physical Education

Models-based Practice in Physical Education

This book offers a comprehensive synthesis of over 40 years of research on models in physical education to suggest Models-based Practice (MbP) as an innovative future approach to physical education. It lays out the ideal conditions for MbP to flourish by situating pedagogical models at the core of physical education programs and allowing space for local agency and the co-construction of practice. Starting from the premise that true MbP does not yet exist the book makes a case for the term pedagogical model over alternatives such as curriculum model and instructional model and explains how learners’ cognitive social affective and psychomotor needs should be organised in ways that are distinctive and unique to each model. It examines the core principles underpinning the pedagogical models that make up MbP including pedagogical models as organising centres for program design and as design specifications for developing local programs. The book also explores how a common structure can be applied to analyse pedagogical models at macro meso and micro levels of discourse. Having created a language through which to talk about pedagogical models and MbP the book concludes by identifying the conditions - some existing and some aspirational - under which MbP can prosper in reforming physical education. An essential read for academics doctoral and post-graduate students and pre-service and in-service teachers Models-based Practice in Physical Education is a vital point of reference for anyone who is interested in pedagogical models and wants to embrace this potential future of physical education.

GBP 38.99
1

Systems Engineering Models Theory Methods and Applications

Systems Engineering Models Theory Methods and Applications

This book presents a comprehensive compilation of practical systems engineering models. The application and recognition of systems engineering is spreading rapidly however there is no book that addresses the availability and usability of systems engineering models. Notable among the models to be included are the V-Model DEJI Model and Waterfall Model. There are other models developed for specific organizational needs which will be identified and presented in a practical template so that other organizations can learn and use them. A better understanding of the models through a comprehensive book will make these models more visible embraced and applied across the spectrum. Visit www. DEJImodel. com for model details. FeaturesCovers applications to both small and large problems Displays decomposition of complex problems into smaller manageable chunks Discusses direct considerations of the pertinent constraints that exist in the problem domain Presents systematic linking of inputs to goals and outputs | Systems Engineering Models Theory Methods and Applications

GBP 44.99
1

Interpreting and Visualizing Regression Models Using Stata

Interpreting and Visualizing Regression Models Using Stata

Interpreting and Visualizing Regression Models Using Stata Second Edition provides clear and simple examples illustrating how to interpret and visualize a wide variety of regression models. Including over 200 figures the book illustrates linear models with continuous predictors (modeled linearly using polynomials and piecewise) interactions of continuous predictors categorical predictors interactions of categorical predictors and interactions of continuous and categorical predictors. The book also illustrates how to interpret and visualize results from multilevel models models where time is a continuous predictor models with time as a categorical predictor nonlinear models (such as logistic or ordinal logistic regression) and models involving complex survey data. The examples illustrate the use of the margins marginsplot contrast and pwcompare commands. This new edition reflects new and enhanced features added to Stata most importantly the ability to label statistical output using value labels associated with factor variables. As a result output regarding marital status is labeled using intuitive labels like Married and Unmarried instead of using numeric values such as 1 and 2. All the statistical output in this new edition capitalizes on this new feature emphasizing the interpretation of results based on variables labeled using intuitive value labels. Additionally this second edition illustrates other new features such as using transparency in graphics to more clearly visualize overlapping confidence intervals and using small sample-size estimation with mixed models. If you ever find yourself wishing for simple and straightforward advice about how to interpret and visualize regression models using Stata this book is for you.

GBP 59.99
1