Aug 132016

Prior Processes and Their Applications: Nonparametric Bayesian Estimation (Springer Series in Statistics)

By (author): Eswar G. Phadia

This book presents a systematic and comprehensive treatment of various prior processes that have been developed over the past four decades for dealing with Bayesian approach to solving selected nonparametric inference problems. This revised edition has been substantially expanded to reflect the current interest in this area. After an overview of different prior processes, it examines the now pre-eminent Dirichlet process and its variants including hierarchical processes, then addresses new processes such as dependent Dirichlet, local Dirichlet, time-varying and spatial processes, all of which exploit the countable mixture representation of the Dirichlet process. It subsequently discusses various neutral to right type processes, including gamma and extended gamma, beta and beta-Stacy processes, and then describes the Chinese Restaurant, Indian Buffet and infinite gamma-Poisson processes, which prove to be very useful in areas such as machine learning, information retrieval and featural modeling. Tailfree and Polya tree and their extensions form a separate chapter, while the last two chapters present the Bayesian solutions to certain estimation problems pertaining to the distribution function and its functional based on complete data as well as right censored data. Because of the conjugacy property of some of these processes, most solutions are presented in closed form.

However, the current interest in modeling and treating large-scale and complex data also poses a problem – the posterior distribution, which is essential to Bayesian analysis, is invariably not in a closed form, making it necessary to resort to simulation. Accordingly, the book also introduces several computational procedures, such as the Gibbs sampler, Blocked Gibbs sampler and slice sampling, highlighting essential steps of algorithms while discussing specific models. In addition, it features crucial steps of proofs and derivations, explains the relationships between different processes and provides further clarifications to promote a deeper understanding. Lastly, it includes a comprehensive list of references, equipping readers to explore further on their own. 

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Aug 112016

Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases

The contributions by epidemic modeling experts describe how mathematical models and statistical forecasting are created to capture the most important aspects of an emerging epidemic.Readers will discover a broad range of approaches to address questions, such as

  • Can we control Ebola via ring vaccination strategies?
  • How quickly should we detect Ebola cases to ensure epidemic control?
  •  What is the likelihood that an Ebola epidemic in West Africa leads to secondary outbreaks in other parts of the world?  
  • When does it matter to incorporate the role of disease-induced mortality on epidemic models?
  •  What is the role of behavior changes on Ebola dynamics? 
  • How can we better understand the control of cholera or Ebola using optimal control theory?
  • How should a population be structured in order to mimic the transmission dynamics of diseases such as chlamydia, Ebola, or cholera?
  • How can we objectively determine the end of an epidemic?
  • How can we use metapopulation models to understand the role of movement restrictions and migration patterns on the spread of infectious diseases?
  • How can we capture the impact of household transmission using compartmental epidemic models?
  • How could behavior-dependent vaccination affect the dynamical outcomes of epidemic models? 

The derivation and analysis of the mathematical models addressing these questions provides a wide-ranging overview of the new approaches being created to better forecast and mitigate emerging epidemics. 

This book will be of interest to researchers in the field of mathematical epidemiology, as well as public health workers.

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Aug 032016

Measurement and Analysis in Transforming Healthcare Delivery: Volume 1: Quantitative Approaches in Health Systems Engineering

By (author): Peter J. Fabri

This volume provides the important concepts necessary for a physician to participate in a reengineering process, develop decision-making skills based on probability and logic rather than “rules,” and to measure and analyze meaningful outcomes of care delivery.  This approach has been developed over ten years in a medical student-based program and has been enthusiastically embraced by medical students without backgrounds in engineering or statistics.  More specifically, this text will introduce physicians to relevant and available computer software, combined with an in depth knowledge of measurement, variation, and uncertainty.  It provides a basis for the transformation of data into information, information into knowledge, and knowledge into wisdom.  The first quarter of the book will address understanding and visualizing data, using statistical and graphic analysis.  The next quarter addresses the fundamentals of applied statistics, and the application of conditional probability to clinical decision making.  The next quarter addresses the four “cornerstones” of modern analytics: regression, classification, association analysis, and clustering.  The final section addresses the identification of outliers and their importance in understanding, the assessment of cause and effect and the limitations associated with retrospective data analysis. This toolbox will prepare the interested physician to actively engage in the identification of problem areas, the design of process-based solutions, and the continuous assessment of outcomes of clinical practice.  Armed with this toolbox, the reader will be “prepared to make a difference” in the rapidly changing world of healthcare delivery.
Measurement and Analysis in Transforming Healthcare Delivery is an excellent resource for general practitioners, health administrators, and all medical professionals interacting with healthcare delivery.
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Aug 032016

Models of Calcium Signalling (Interdisciplinary Applied Mathematics)

By (author): Geneviève Dupont, Martin Falcke, Vivien Kirk, James Sneyd

This book discusses the ways in which mathematical, computational, and modelling methods can be used to help understand the dynamics of intracellular calcium. The concentration of free intracellular calcium is vital for controlling a wide range of cellular processes, and is thus of great physiological importance. However, because of the complex ways in which the calcium concentration varies, it is also of great mathematical interest.This book presents the general modelling theory as well as a large number of specific case examples, to show how mathematical modelling can interact with experimental approaches, in an interdisciplinary and multifaceted approach to the study of an important physiological control mechanism.

Geneviève Dupont is FNRS Research Director at the Unit of Theoretical Chronobiology of the Université Libre de Bruxelles; Martin Falcke is head of the Mathematical Cell Physiology group at the Max Delbrück Center for Molecular Medicine, Berlin; Vivien Kirk is an Associate Professor in the Department of Mathematics at the University of Auckland, New Zealand; James Sneyd is a Professor in the Department of Mathematics at The University of Auckland, New Zealand. 

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Jul 152016

Practical Statistics for Medical Research (Chapman & Hall/CRC Texts in Statistical Science)

Features: CRC Press
By (author): Douglas G. Altman

Most medical researchers, whether clinical or non-clinical, receive some background in statistics as undergraduates. However, it is most often brief, a long time ago, and largely forgotten by the time it is needed. Furthermore, many introductory texts fall short of adequately explaining the underlying concepts of statistics, and often are divorced from the reality of conducting and assessing medical research.

Practical Statistics for Medical Research is a problem-based text for medical researchers, medical students, and others in the medical arena who need to use statistics but have no specialized mathematics background.

The author draws on twenty years of experience as a consulting medical statistician to provide clear explanations to key statistical concepts, with a firm emphasis on practical aspects of designing and analyzing medical research. The text gives special attention to the presentation and interpretation of results and the many real problems that arise in medical research.
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Jul 072016

Computing Characterizations of Drugs for Ion Channels and Receptors Using Markov Models (Lecture Notes in Computational Science and Engineering)

By (author): Aslak Tveito, Glenn T. Lines

Flow of ions through voltage gated channels can be represented theoretically using stochastic differential equations where the gating mechanism is represented by a Markov model. The flow through a channel can be manipulated using various drugs, and the effect of a given drug can be reflected by changing the Markov model. These lecture notes provide an accessible introduction to the mathematical methods needed to deal with these models. They emphasize the use of numerical methods and provide sufficient details for the reader to implement the models and thereby study the effect of various drugs.  Examples in the text include stochastic calcium release from internal storage systems in cells, as well as stochastic models of the transmembrane potential. Well known Markov models are studied and a systematic approach to including the effect of mutations is presented. Lastly, the book shows how to derive the optimal properties of a theoretical model of a drug for a given mutation defined in terms of a Markov model.

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Jun 202016

Modeling Discrete Time-to-Event Data (Springer Series in Statistics)

By (author): Gerhard Tutz, Matthias Schmid

This book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are explained. Each section includes a set of exercises on the respective topics. Various functions and tools for the analysis of discrete survival data are collected in the R package discSurv that accompanies the book. 

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Jun 192016

Beyond Significance Testing: Statistics Reform in the Behavioral Sciences

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By (author): Rex B. Kline

This accessibly written book reviews the controversy about significance testing, which has now crossed various disciplines as diverse as psychology, ecology, commerce, education, and biology, among others. It also introduces readers to alternative methods, especially effect size estimation (at both the group and case levels) and interval estimation (confidence intervals) in comparative studies. Basics of bootstrapping and Bayesian estimation are also considered. Research examples from substance abuse, education, learning, and other areas illustrate how to apply these methods.
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Jun 152016

mODa 11 – Advances in Model-Oriented Design and Analysis: Proceedings of the 11th International Workshop in Model-Oriented Design and Analysis held in … 12-17, 2016 (Contributions to Statistics)

This volume contains pioneering contributions to both the theory and practice of optimal experimental design. Topics include the optimality of designs in linear and nonlinear models, as well as designs for correlated observations and for sequential experimentation. There is an emphasis on applications to medicine, in particular, to the design of clinical trials. Scientists from Europe, the US, Asia, Australia and Africa contributed to this volume of papers from the 11th Workshop on Model Oriented Design and Analysis.

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Jun 072016

Group-Sequential Clinical Trials with Multiple Co-Objectives (SpringerBriefs in Statistics)

By (author): Toshimitsu Hamasaki, Koko Asakura, Scott R. Evans, Toshimitsu Ochiai

This book focuses on group sequential methods for clinical trials with co-primary endpoints based on the decision-making frameworks for: (1) rejecting the null hypothesis (stopping for efficacy), (2) rejecting the alternative hypothesis (stopping for futility), and (3) rejecting the null or alternative hypothesis (stopping for either futility or efficacy), where the trial is designed to evaluate whether the intervention is superior to the control on all endpoints. For assessing futility, there are two fundamental approaches, i.e., the decision to stop for futility based on the conditional probability of rejecting the null hypothesis, and the other based on stopping boundaries using group sequential methods. In this book, the latter approach is discussed. The book also briefly deals with the group sequential methods for clinical trials designed to evaluate whether the intervention is superior to the control on at least one endpoint. In addition, the book describes sample size recalculation and the resulting effect on power and type I error rate. The book also describes group sequential strategies for three-arm clinical trials to demonstrate the non-inferiority of experimental intervention to actively control and to assess the assay sensitivity to placebo control.
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