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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Statistical Development of Quality in Medicine

 Statistics  Comments Off on Statistical Development of Quality in Medicine
Feb 022019
 

The promotion of standards and guidelines to advance quality assurance and control is an integral part of the health care sector. Quantitative methods are needed to monitor, control and improve the quality of medical processes. Statistical Development of Quality in Medicine presents the statistical concepts behind the application of industrial quality control methods. Filled with numerous case studies and worked examples, the text enables the reader to choose the relevant control chart, to critically apply it, improve it if necessary, and monitor its stability. Furthermore, the reader is provided with the necessary background to critically assess the literature on the application of control charts and risk adjustment and to apply the findings. Contains a user-friendly introduction, setting out the necessary statistical concepts used in the field. Uses numerous real-life case studies from the literature and the authors’ own research as the backbone of the text. Provides a supplementary website featuring problems and answers drawn from the book, alongside examples in Statgraphics. The accessible style of Statistical Development of in Clinical Medicine invites a large readership. It is primarily aimed at health care officials, and personnel responsible for developing and controlling the quality of health care services. However, it is also ideal for statisticians working with health care problems, diagnostic and pharmaceutical companies, and graduate students of quality control.Content: Chapter 1 Theory of Statistical Process Control (pages 9–36): Chapter 2 Shewhart Control Charts (pages 37–77): Chapter 3 Time?Weighted Control Charts (pages 78–91): Chapter 4 Control Charts for Autocorrelated Data (pages 92–109): Chapter 5 Tools for Risk Adjustment (pages 111–148): Chapter 6 Risk?Adjusted Control Charts (pages 149–163): Chapter 7 Risk?Adjusted Comparison of Healthcare Providers (pages 164–181): Chapter 8 Learning Curves (pages 183–194): Chapter 9 Assessing the Quality of Clinical Processes (pages 195–215):
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Statistical Methods for Microarray Data Analysis: Methods and Protocols

 Statistics  Comments Off on Statistical Methods for Microarray Data Analysis: Methods and Protocols
Nov 072018
 


Microarrays for simultaneous measurement of redundancy of RNA species are used in fundamental biology as well as in medical research. Statistically,a microarray may be considered as an observation of very high dimensionality equal to the number of expression levels measured on it.

In Statistical Methods for Microarray Data Analysis: Methods and Protocols, expert researchers in the field detail many methods and techniques used to study microarrays, guiding the reader from microarray technology to statistical problems of specific multivariate data analysis. Written in the highly successful Methods in Molecular Biology™ series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results in the laboratory. Thorough and intuitive, Statistical Methods for Microarray Data Analysis: Methods and Protocols aids scientists in continuing to study microarrays and the most current statistical methods.

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The statistical evaluation of medical tests for classification and prediction

 Statistics  Comments Off on The statistical evaluation of medical tests for classification and prediction
Oct 132018
 

This book describes statistical concepts and techniques for evaluating medical diagnostic tests and biomarkers for detecting disease. More generally, the techniques pertain to the statistical classification problem for predicting a dichotomous outcome. Measures for quantifying test accuracy are described including sensitivity, specificity, predictive values, diagnostic likelihood ratios and the Receiver Operating Characteristic Curve that is commonly used for continuous and ordinal valued tests. Statistical procedures are presented for estimating and comparing them. Regression frameworks for assessing factors that influence test accuracy and for comparing tests while adjusting for such factors are presented. This book presents many worked examples of real data and should be of interest to practicing statisticians or quantitative researchers involved in the development of tests for classification or prediction in medicine.
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Applied Biostatistical Principles and Concepts: Clinicians’ Guide to Data Analysis and Interpretation

 Statistics  Comments Off on Applied Biostatistical Principles and Concepts: Clinicians’ Guide to Data Analysis and Interpretation
Sep 252018
 

The past three decades have witnessed modern advances in statistical modeling and evidence discovery in biomedical, clinical, and population-based research. With these advances come the challenges in accurate model stipulation and application of models in scientific evidence discovery

Applied Biostatistical Principles and Concepts provides practical knowledge using biological and biochemical specimen/samples in order to understand health and disease processes at cellular, clinical, and population levels. Concepts and techniques provided will help researchers design and conduct studies, then translate data from bench to clinics in attempt to improve the health of patients and populations.

This book is suitable for both clinicians and health or biological sciences students. It presents the reality in statistical modelling of health research data in a concise manner that will address the issue of “big data” type I error tolerance and probability value, effect size and confidence interval for precision, effect measure modification and interaction as well as confounders, thus allowing for more valid inferences and yielding results that are more reliable, valid and accurate.

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