Apr 292017

Bioequivalence and Statistics in Clinical Pharmacology, Second Edition (Chapman & Hall/CRC Biostatistics Series)

By (author): Scott D. Patterson, Byron Jones

Maintaining a practical perspective, Bioequivalence and Statistics in Clinical Pharmacology, Second Edition explores statistics used in day-to-day clinical pharmacology work. The book is a starting point for those involved in such research and covers the methods needed to design, analyze, and interpret bioequivalence trials; explores when, how, and why these studies are performed as part of drug development; and demonstrates the methods using real world examples.

Drawing on knowledge gained directly from working in the pharmaceutical industry, the authors set the stage by describing the general role of statistics. Once the foundation of clinical pharmacology drug development, regulatory applications, and the design and analysis of bioequivalence trials are established, including recent regulatory changes in design and analysis and in particular sample-size adaptation, they move on to related topics in clinical pharmacology involving the use of cross-over designs. These include, but are not limited to, safety studies in Phase I, dose-response trials, drug interaction trials, food-effect and combination trials, QTc and other pharmacodynamic equivalence trials, proof-of-concept trials, dose-proportionality trials, and vaccines trials.

This second edition addresses several recent developments in the field, including new chapters on adaptive bioequivalence studies, scaled average bioequivalence testing, and vaccine trials.

Purposefully designed to be instantly applicable, Bioequivalence and Statistics in Clinical Pharmacology, Second Edition provides examples of SAS and R code so that the analyses described can be immediately implemented. The authors have made extensive use of the proc mixed procedures available in SAS.

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Apr 222017

Statistical and Computational Methods in Brain Image Analysis (Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Series)

By (author): Moo K. Chung

The massive amount of nonstandard high-dimensional brain imaging data being generated is often difficult to analyze using current techniques. This challenge in brain image analysis requires new computational approaches and solutions. But none of the research papers or books in the field describe the quantitative techniques with detailed illustrations of actual imaging data and computer codes. Using MATLAB® and case study data sets, Statistical and Computational Methods in Brain Image Analysis is the first book to explicitly explain how to perform statistical analysis on brain imaging data.

The book focuses on methodological issues in analyzing structural brain imaging modalities such as MRI and DTI. Real imaging applications and examples elucidate the concepts and methods. In addition, most of the brain imaging data sets and MATLAB codes are available on the author’s website.

By supplying the data and codes, this book enables researchers to start their statistical analyses immediately. Also suitable for graduate students, it provides an understanding of the various statistical and computational methodologies used in the field as well as important and technically challenging topics.

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Apr 162017

Emerging Non-Clinical Biostatistics in Biopharmaceutical Development and Manufacturing (Chapman & Hall/CRC Biostatistics Series)

By (author): Harry Yang

The premise of Quality by Design (QbD) is that the quality of the pharmaceutical product should be based upon a thorough understanding of both the product and the manufacturing process. This state-of-the-art book provides a single source of information on emerging statistical approaches to QbD and risk-based pharmaceutical development. A comprehensive resource, it combines in-depth explanations of advanced statistical methods with real-life case studies that illustrate practical applications of these methods in QbD implementation.

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Apr 152017

Phase II Clinical Development of New Drugs (ICSA Book Series in Statistics)

By (author): Naitee Ting, Ding-Geng Chen, Shuyen Ho, Joseph C. Cappelleri

This book focuses on how to appropriately plan and develop a Phase II program, and how to design Phase II clinical trials and analyze their data. It provides a comprehensive overview of the entire drug development process and highlights key questions that need to be addressed for the successful execution of Phase II, so as to increase its success in Phase III and for drug approval. Lastly it warns project team members of the common potential pitfalls and offers tips on how to avoid them.
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Apr 062017

Key Statistical Concepts in Clinical Trials for Pharma (SpringerBriefs in Pharmaceutical Science & Drug Development)

Features: Used Book in Good Condition
By (author): J. Rick Turner

This Brief discusses key statistical concepts that facilitate the inferential analysis of data collected from a group of individuals participating in a pharmaceutical clinical trial, the estimation of their clinical significance in the general population of individuals likely to be prescribed the drug if approved, and the related decision-making that occurs at both the public health level (by regulatory agencies when deciding whether or not to approve a new drug for marketing) and the individual patient level (by physicians and their patients when deciding whether or not the patient should be prescribed a drug that is on the market). These concepts include drug safety and efficacy, statistical significance, clinical significance, and benefit-risk balance.
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Mar 222017

Disease Mapping with WinBUGS and MLwiN

By (author): Andrew B. Lawson, William J. Browne, Carmen L. Vidal Rodeiro

Disease mapping involves the analysis of geo-referenced disease incidence data and has many applications, for example within resource allocation, cluster alarm analysis, and ecological studies. There is a real need amongst public health workers for simpler and more efficient tools for the analysis of geo-referenced disease incidence data. Bayesian and multilevel methods provide the required efficiency, and with the emergence of software packages – such as WinBUGS and MLwiN – are now easy to implement in practice.

  • Provides an introduction to Bayesian and multilevel modelling in disease mapping.
  • Adopts a practical approach, with many detailed worked examples.
  • Includes introductory material on WinBUGS and MLwiN.
  • Discusses three applications in detail – relative risk estimation, focused clustering, and ecological analysis.
  • Suitable for public health workers and epidemiologists with a sound statistical knowledge.
  • Supported by a Website featuring data sets and WinBUGS and MLwiN programs.

Disease Mapping with WinBUGS and MLwiN  provides a practical introduction to the use of software for disease mapping for researchers, practitioners and graduate students from statistics, public health and epidemiology who analyse disease incidence data.

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Mar 032017

Mahajan’s Methods in Biostatistics for Medical Students and Research Workers

By (author): Arun Bhadra Khanal

Brand New Book in Perfect Condition.Fast Shipping with tracking number.
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Jan 222017

Computational Intelligence Methods for Bioinformatics and Biostatistics: 12th International Meeting, CIBB 2015, Naples, Italy, September 10-12, 2015, … Papers (Lecture Notes in Computer Science)

This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2015, held in Naples, Italy, in September, 2015.

The  21 revised full papers presented were carefully reviewed and selected from 24 submissions. They present problems concerning computational techniques in bioinformatics, systems biology and medical informatics discussing cutting edge methodologies and accelerate life science discoveries, as well as novel challenges with an high impact on molecular biology and translational medicine.

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Jan 082017

Statistical Techniques for Neuroscientists (Frontiers in Neuroscience)

Statistical Techniques for Neuroscientists introduces new and useful methods for data analysis involving simultaneous recording of neuron or large cluster (brain region) neuron activity. The statistical estimation and tests of hypotheses are based on the likelihood principle derived from stationary point processes and time series. Algorithms and software development are given in each chapter to reproduce the computer simulated results described therein.

The book examines current statistical methods for solving emerging problems in neuroscience. These methods have been applied to data involving multichannel neural spike train, spike sorting, blind source separation, functional and effective neural connectivity, spatiotemporal modeling, and multimodal neuroimaging techniques. The author provides an overview of various methods being applied to specific research areas of neuroscience, emphasizing statistical principles and their software. The book includes examples and experimental data so that readers can understand the principles and master the methods.

The first part of the book deals with the traditional multivariate time series analysis applied to the context of multichannel spike trains and fMRI using respectively the probability structures or likelihood associated with time-to-fire and discrete Fourier transforms (DFT) of point processes. The second part introduces a relatively new form of statistical spatiotemporal modeling for fMRI and EEG data analysis. In addition to neural scientists and statisticians, anyone wishing to employ intense computing methods to extract important features and information directly from data rather than relying heavily on models built on leading cases such as linear regression or Gaussian processes will find this book extremely helpful.

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Jan 062017

Essentials of a Successful Biostatistical Collaboration (Chapman & Hall/CRC Biostatistics Series)

By (author): Arul Earnest

The aim of this book is to equip biostatisticians and other quantitative scientists with the necessary skills, knowledge, and habits to collaborate effectively with clinicians in the healthcare field. The book provides valuable insight on where to look for information and material on sample size and statistical techniques commonly used in clinical research, and on how best to communicate with clinicians. It also covers the best practices to adopt in terms of project, time, and data management; relationship with collaborators; etc.

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