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  • Group-Sequential Clinical Trials with Multiple Co-Objectives

    Series series Mathematics and Statistics (R0)
    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 ... Read more

    $62.99 USD

  • Sample Size Determination in Clinical Trials with Multiple Endpoints

    Series series Mathematics and Statistics (R0)
    This book integrates recent methodological developments for calculating the sample size and power in trials with more than one endpoint considered as multiple primary or co-primary, offering an important reference work for statisticians working in this area.The determination of sample size and the evaluation of power are fundamental and critical elements in the design of clinical trials. If the ... Read more

    $71.99 USD

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    Series series Mathematics and Statistics (R0)
    This book provides a comprehensive introduction to performing meta-analysis using the statistical software R. It is intended for quantitative researchers and students in the medical and social sciences who wish to learn how to perform meta-analysis with R. As such, the book introduces the key concepts and models used in meta-analysis. It also includes chapters on the following advanced topics: ... Read more

    $58.99 USD

  • New Theory of Discriminant Analysis After R. Fisher

    Advanced Research by the Feature Selection Method for Microarray Data

    Series series Mathematics and Statistics (R0)
    This is the first book to compare eight LDFs by different types of datasets, such as Fisher’s iris data, medical data with collinearities, Swiss banknote data that is a linearly separable data (LSD), student pass/fail determination using student attributes, 18 pass/fail determinations using exam scores, Japanese automobile data, and six microarray datasets (the datasets) that are LSD. We developed ... Read more

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  • Recent Advances in Robust Statistics: Theory and Applications

    Series series Springer Nature Proceedings excluding Computer Science
    This book offers a collection of recent contributions and emerging ideas in the areas of robust statistics presented at the International Conference on Robust Statistics 2015 (ICORS 2015) held in Kolkata during 12–16 January, 2015. The book explores the applicability of robust methods in other non-traditional areas which includes the use of new techniques such as skew and mixture of skew ... Read more

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  • The Science of Fear

    How the Culture of Fear Manipulates Your Brain

    “An invaluable resource for anyone who aspires to think clearly” (The Guardian) from the New York Times bestselling author of Superforecasting and Future BabbleFrom terror attacks to collapsing economies, from painkiller epidemics to mass gun violence and poisonous toys from China, our list of fears seems to be exploding. Yet we are the safest and healthiest humans in history. Why are we so ... Read more

    $13.99 USD

  • Statistics in Food Science and Nutrition

    Series series Chemistry and Material Science (R0)
    Many statistical innovations are linked to applications in food science. For example, the student t-test (a statistical method) was developed to monitor the quality of stout at the Guinness Brewery and multivariate statistical methods are applied widely in the spectroscopic analysis of foods. Nevertheless, statistical methods are most often associated with engineering, mathematics, and the medical ... Read more

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  • Applied Survival Analysis Using R

    by Dirk F. Moore ...
    Series series Mathematics and Statistics (R0)
    Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many ... Read more

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  • Contingency Table Analysis

    Methods and Implementation Using R

    by Maria Kateri ...
    Series series Mathematics and Statistics (R0)
    Contingency tables arise in diverse fields, including life sciences, education, social and political sciences, notably market research and opinion surveys. Their analysis plays an essential role in gaining insight into structures of the quantities under consideration and in supporting decision making.Combining both theory and applications, this book presents models and methods for the analysis of ... Read more

    $49.49 USD

  • Applied Multivariate Statistics with R

    Series series Mathematics and Statistics (R0)
    This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program R, Professor Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays, linear algebra, univariate, ... Read more

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  • An Introduction to Statistics with Python

    With Applications in the Life Sciences

    Series series Mathematics and Statistics (R0)
    This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. Working code and data for Python solutions for each test, together with easy-to-follow Python examples, can be ... Read more

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  • Machine Learning in Medicine - Cookbook

    Series series Mathematics and Statistics (R0)
    The amount of data in medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional methods of data analysis have difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing.Obviously, ... Read more

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