Methods and Applications of Statistics in Clinical Trials, Volume 1: Concepts, Principles, Trials, and Designs

 Statistics  Comments Off on Methods and Applications of Statistics in Clinical Trials, Volume 1: Concepts, Principles, Trials, and Designs
Jul 122019
 

A complete guide to the key statistical concepts essential for the design and construction of clinical trials
As the newest major resource in the field of medical research, Methods and Applications of Statistics in Clinical Trials, Volume 1: Concepts, Principles, Trials, and Designs presents a timely and authoritative reviewof the central statistical concepts used to build clinical trials that obtain the best results. The referenceunveils modern approaches vital to understanding, creating, and evaluating data obtained throughoutthe various stages of clinical trial design and analysis.
Accessible and comprehensive, the first volume in a two-part set includes newly-written articles as well as established literature from the Wiley Encyclopedia of Clinical Trials. Illustrating a variety of statistical concepts and principles such as longitudinal data, missing data, covariates, biased-coin randomization, repeated measurements, and simple randomization, the book also provides in-depth coverage of the various trial designs found within phase I-IV trials. Methods and Applications of Statistics in Clinical Trials, Volume 1: Concepts, Principles, Trials, and Designs also features:
Detailed chapters on the type of trial designs, such as adaptive, crossover, group-randomized, multicenter, non-inferiority, non-randomized, open-labeled, preference, prevention, and superiority trials
Over 100 contributions from leading academics, researchers, and practitioners
An exploration of ongoing, cutting-edge clinical trials on early cancer and heart disease, mother-to-child human immunodeficiency virus transmission trials, and the AIDS Clinical Trials Group
Methods and Applications of Statistics in Clinical Trials, Volume 1: Concepts, Principles, Trials, and Designs is an excellent reference for researchers, practitioners, and students in the fields of clinicaltrials, pharmaceutics, biostatistics, medical research design, biology, biomedicine, epidemiology,and public health.

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Statistics for Nursing and Allied Health

 Nursing, Statistics  Comments Off on Statistics for Nursing and Allied Health
Jun 132019
 

This introductory textbook explores the role of research in health care and focuses in particular on the importance of organizing and describing research data using basic statistics. The goal of the text is to teach students how to analyze data and present the results of evidence-based data analysis. Based on the commonly-used SPSS software, a comprehensive range of statistical techniques—both parametric and non-parametric—are presented and explained. Examples are given from nursing, health administration, and health professions, followed by an opportunity for students to immediately practice the technique.
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Fundamental Statistics for the Behavioral Sciences

 Statistics  Comments Off on Fundamental Statistics for the Behavioral Sciences
Jun 132019
 

David Howell’s practical approach focuses on the context of statistics in behavioral research, with an emphasis on looking before leaping; investigating the data before jumping into a test. This provides you with an understanding of the logic behind the statistics: why and how certain methods are used rather than just doing techniques by rote. Learn faster and understand more because Howell’s texts moves you beyond number crunching, allowing you to discover the meaning of statistical results and how they relate to the research questions being asked.
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Controversial Statistical Issues in Clinical Trials (Chapman & Hall CRC Biostatistics Series)

 Statistics  Comments Off on Controversial Statistical Issues in Clinical Trials (Chapman & Hall CRC Biostatistics Series)
Jun 032019
 


In clinical trial practice, controversial statistical issues inevitably occur regardless of the compliance with good statistical practice and good clinical practice. But by identifying the causes of the issues and correcting them, the study objectives of clinical trials can be better achieved. Controversial Statistical Issues in Clinical Trials covers commonly encountered controversial statistical issues in clinical trials and, whenever possible, makes recommendations to resolve these problems. The book focuses on issues occurring at various stages of clinical research and development, including early-phase clinical development (such as bioavailability/bioequivalence), bench-to-bedside translational research, and late-phase clinical development. Numerous examples illustrate the impact of these issues on the evaluation of the safety and efficacy of the test treatment under investigation. The author also offers recommendations regarding possible resolutions of the problems. Written by one of the preeminent experts in the field, this book provides a useful desk reference and state-of-the art examination of problematic issues in clinical trials for scientists in the pharmaceutical industry, medical/statistical reviewers in government regulatory agencies, and researchers and students in academia.
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Statistics in Drug Research: Methodologies and Recent Developments (Chapman & Hall/CRC Biostatistics Series)

 Statistics  Comments Off on Statistics in Drug Research: Methodologies and Recent Developments (Chapman & Hall/CRC Biostatistics Series)
May 142019
 


Emphasizing the role of good statistical practices (GSP) in drug research and formulation, this book outlines important statistics applications for each stage of pharmaceutical development to ensure the valid design, analysis, and assessment of drug products under investigation and establish the safety and efficacy of pharmaceutical compounds. Coverage include statistical techniques for assay validation and evaluation of drug performance characteristics, testing population/individual bioequivalence and in vitro bioequivalence according to the most recent FDA guidelines, basic considerations for the design and analysis of therapeutic equivalence and noninferiority trials.
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Statistical learning for biomedical data

 Statistics  Comments Off on Statistical learning for biomedical data
Apr 282019
 

Machine generated contents note: 1.Prologue — 1.1.Machines that learn — some recent history — 1.2.Twenty canonical questions — 1.3.Outline of the book — 1.4.A comment about example datasets — 1.5.Software — Note — 2.The landscape of learning machines — 2.1.Introduction — 2.2.Types of data for learning machines — 2.3.Will that be supervised or unsupervised? — 2.4.An unsupervised example — 2.5.More lack of supervision — where are the parents? — 2.6.Engines, complex and primitive — 2.7.Model richness means what, exactly? — 2.8.Membership or probability of membership? — 2.9.A taxonomy of machines? — 2.10.A note of caution — one of many — 2.11.Highlights from the theory — Notes — 3.A mangle of machines — 3.1.Introduction — 3.2.Linear regression — 3.3.Logistic regression — 3.4.Linear discriminant — 3.5.Bayes classifiers [-] regular and naive — 3.6.Logic regression — 3.7.k-Nearest neighbors — 3.8.Support vector machines — 3.9.Neural networks — 3.10.Boosting — 3.11.Evolutionary and genetic algorithms — Notes — 4.Three examples and several machines — 4.1.Introduction — 4.2.Simulated cholesterol data — 4.3.Lupus data — 4.4.Stroke data — 4.5.Biomedical means unbalanced — 4.6.Measures of machine performance — 4.7.Linear analysis of cholesterol data — 4.8.Nonlinear analysis of cholesterol data — 4.9.Analysis of the lupus data — 4.10.Analysis of the stroke data — 4.11.Further analysis of the lupus and stroke data — Notes — 5.Logistic regression — 5.1.Introduction — 5.2.Inside and around the model — 5.3.Interpreting the coefficients — 5.4.Using logistic regression as a decision rule — 5.5.Logistic regression applied to the cholesterol data — 5.6.A cautionary note — 5.7.Another cautionary note — 5.8.Probability estimates and decision rules — 5.9.Evaluating the goodness-of-fit of a logistic regression model — 5.10.Calibrating a logistic regression — 5.11.Beyond calibration — 5.12.Logistic regression and reference models — Notes — 6.A single decision tree — 6.1.Introduction — 6.2.Dropping down trees — 6.3.Growing a tree — 6.4.Selecting features, making splits — 6.5.Good split, bad split — 6.6.Finding good features for making splits — 6.7.Misreading trees — 6.8.Stopping and pruning rules — 6.9.Using functions of the features — 6.10.Unstable trees? — 6.11.Variable importance — growing on trees? — 6.12.Permuting for importance — 6.13.The continuing mystery of trees — 7.Random Forests — trees everywhere — 7.1.Random Forests in less than five minutes — 7.2.Random treks through the data — 7.3.Random treks through the features — 7.4.Walking through the forest — 7.5.Weighted and unweighted voting — 7.6.Finding subsets in the data using proximities — 7.7.Applying Random Forests to the Stroke data — 7.8.Random Forests in the universe of machines — Notes — 8.Merely two variables — 8.1.Introduction — 8.2.Understanding correlations — 8.3.Hazards of correlations — 8.4.Correlations big and small — Notes — 9.More than two variables — 9.1.Introduction — 9.2.Tiny problems, large consequences — 9.3.Mathematics to the rescue? — 9.4.Good models need not be unique — 9.5.Contexts and coefficients — 9.6.Interpreting and testing coefficients in models — 9.7.Merging models, pooling lists, ranking features — Notes — 10.Resampling methods — 10.1.Introduction — 10.2.The bootstrap — 10.3.When the bootstrap works — 10.4.When the bootstrap doesn’t work — 10.5.Resampling from a single group in different ways — 10.6.Resampling from groups with unequal sizes — 10.7.Resampling from small datasets — 10.8.Permutation methods — 10.9.Still more on permutation methods — Note — 11.Error analysis and model validation — 11.1.Introduction — 11.2.Errors? What errors? — 11.3.Unbalanced data, unbalanced errors — 11.4.Error analysis for a single machine — 11.5.Cross-validation error estimation — 11.6.Cross-validation or cross-training? — 11.7.The leave-one-out method — 11.8.The out-of-bag method — 11.9.Intervals for error estimates for a single machine — 11.10.Tossing random coins into the abyss — 11.11.Error estimates for unbalanced data — 11.12.Confidence intervals for comparing error values — 11.13.Other measures of machine accuracy — 11.14.Benchmarking and winning the lottery — 11.15.Error analysis for predicting continuous outcomes — Notes — 12.Ensemble methods [–] let’s take a vote — 12.1.Pools of machines — 12.2.Weak correlation with outcome can be good enough — 12.3.Model averaging — Notes — 13.Summary and conclusions — 13.1.Where have we been? — 13.2.So many machines — 13.3.Binary decision or probability estimate? — 13.4.Survival machines? Risk machines? — 13.5.And where are we going?
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Statistics Plain and Simple

 Statistics  Comments Off on Statistics Plain and Simple
Apr 112019
 


This straightforward, concise, conversational introduction to statistics presents a plain-and-simple overview of statistics that is sparing in its use of jargon. Readers develop a strong awareness of the interaction between statistical methods and research methods along with a solid working knowledge of basic statistical cautions in research design, an understanding of the concept of significance, and the critical thinking skills necessary to apply these ideas. Available with InfoTrac Student Collections http://gocengage.com/infotrac.
 
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Basic Statistics and Epidemiology: A Practical Guide, Fourth Edition

 Epidemiology, Statistics  Comments Off on Basic Statistics and Epidemiology: A Practical Guide, Fourth Edition
Feb 262019
 

Basic Statistics and Epidemiology is a straightforward primer in basic statistics that emphasizes its practical use in epidemiology and public health, providing an understanding of essential topics such as study design, data analysis and statistical methods used in the execution of medical research.

Assuming no prior knowledge, the clarity of the text and care of presentation ensure those new to, or challenged by, these topics are given a thorough introduction without being overwhelmed by unnecessary detail. An understanding and appreciation of statistics is central to ensuring that professional practice is based on the best available evidence, in order to treat and help most appropriately the wider community. By reading this book, students, researchers, doctors, nurses and health managers will have the knowledge necessary to understand and apply the tools of statistics and epidemiology to their own practice.

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Statistics at Square One, 11th edition

 Statistics  Comments Off on Statistics at Square One, 11th edition
Feb 252019
 

The new edition of this international bestseller continues to throw light on the world of statistics for health care professionals and medical students.Revised throughout, the 11th edition features new material in the areas ofrelative risk, absolute risk and numbers needed to treatdiagnostic tests, sensitivity, specificity, ROC curvesfree statistical softwareThe popular self-testing exercises at the end of every chapter are strengthened by the addition of new sections on reading and reporting statistics and formula appreciation.

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Biostatistical Methods

 Statistics  Comments Off on Biostatistical Methods
Feb 162019
 

The use of biostatistical techniques in molecular biology has grown tremendously in recent years and is now essential for the correct interpretation of a wide variety of laboratory studies. In Biostatistical Methods, a panel of leading biostatisticians and biomedical researchers describe all the key techniques used to solve commonly occurring analytical problems in molecular biology, and demonstrate how these methods can identify new markers for exposure to a risk factor, or for determining disease outcomes. Major areas of application include microarray analysis, proteomic studies, image quantitation, determining new disease biomarkers, and designing studies with adequate levels of statistical power. In the case of genetic effects in human populations, the authors describe sophisticated statistical methods to control the overall false-positive rate when many statistical tests are used in linking particular alleles to the occurrence of disease. Other methods discussed are those used to validate statistical approaches for analyzing the E-D association, to study the associations between disease and the inheritance of particular genetic variants, and to examine real data sets. There are also useful recommendations for statistical and data management software (JAVA, Oracle, S-Plus, STATA, and SAS) . Accessible, state-of-the-art, and highly practical,  Methods provides an excellent starting point both for statisticians just beginning work on problems in molecular biology, and for all molecular biologists who want to use biostatistics in genetics research designed to uncover the causes and treatments of disease.
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