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)

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

Features: Used Book in Good Condition
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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May 312016

Complex Surveys: Analysis of Categorical Data

By (author): Parimal Mukhopadhyay

The primary objective of this book is to study some of the research topics in the area of analysis of complex surveys which have not been covered in any book yet. It discusses the analysis of categorical data using three models: a full model, a log-linear model and a logistic regression model. It is a valuable resource for survey statisticians and practitioners in the field of sociology, biology, economics, psychology and other areas who have to use these procedures in their day-to-day work. It is also useful for courses on sampling and complex surveys at the upper-undergraduate and graduate levels. 
The importance of sample surveys today cannot be overstated. From voters’ behaviour to fields such as industry, agriculture, economics, sociology, psychology, investigators generally resort to survey sampling to obtain an assessment of the behaviour of the population they are interested in. Many large-scale sample surveys collect data using complex survey designs like multistage stratified cluster designs. The observations using these complex designs are not independently and identically distributed – an assumption on which the classical procedures of inference are based. This means that if classical tests are used for the analysis of such data, the inferences obtained will be inconsistent and often invalid. For this reason, many modified test procedures have been developed for this purpose over the last few decades.
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May 232016

Biostatistics in Public Health Using STATA

By (author): Erick L. Suárez, Cynthia M. Pérez, Graciela M. Nogueras, Camille Moreno-Gorrín

Striking a balance between theory, application, and programming, Biostatistics in Public Health Using STATA is a user-friendly guide to applied statistical analysis in public health using STATA version 14. The book supplies public health practitioners and students with the opportunity to gain expertise in the application of statistics in epidemiologic studies.

The book shares the authors’ insights gathered through decades of collective experience teaching in the academic programs of biostatistics and epidemiology. Maintaining a focus on the application of statistics in public health, it facilitates a clear understanding of the basic commands of STATA for reading and saving databases.

The book includes coverage of data description, graph construction, significance tests, linear regression models, analysis of variance, categorical data analysis, logistic regression model, poisson regression model, survival analysis, analysis of correlated data, and advanced programming in STATA.

Each chapter is based on one or more research problems linked to public health. Additionally, every chapter includes exercise sets for practicing concepts and exercise solutions for self or group study. Several examples are presented that illustrate the applications of the statistical method in the health sciences using epidemiologic study designs.

Presenting high-level statistics in an accessible manner across research fields in public health, this book is suitable for use as a textbook for biostatistics and epidemiology courses or for consulting the statistical applications in public health.

For readers new to STATA, the first three chapters should be read sequentially, as they form the basis of an introductory course to this software.

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