LOGISTIC REGRESSION A SELF LEARNING TEXT STATISTICS FOR BIOLOGY AND HEALTH

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Logistic Regression

Author : David G. Kleinbaum
ISBN : 9781441917423
Genre : Medical
File Size : 53.14 MB
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This is the third edition of this text on logistic regression methods, originally published in 1994, with its second e- tion published in 2002. As in the first two editions, each chapter contains a pres- tation of its topic in “lecture?book” format together with objectives, an outline, key formulae, practice exercises, and a test. The “lecture book” has a sequence of illust- tions, formulae, or summary statements in the left column of each page and a script (i. e. , text) in the right column. This format allows you to read the script in conjunction with the illustrations and formulae that highlight the main points, formulae, or examples being presented. This third edition has expanded the second edition by adding three new chapters and a modified computer appendix. We have also expanded our overview of mod- ing strategy guidelines in Chap. 6 to consider causal d- grams. The three new chapters are as follows: Chapter 8: Additional Modeling Strategy Issues Chapter 9: Assessing Goodness of Fit for Logistic Regression Chapter 10: Assessing Discriminatory Performance of a Binary Logistic Model: ROC Curves In adding these three chapters, we have moved Chaps. 8 through 13 from the second edition to follow the new chapters, so that these previous chapters have been ren- bered as Chaps. 11–16 in this third edition.
Category: Medical

Survival Analysis

Author : David G. Kleinbaum
ISBN : 9781441966469
Genre : Medical
File Size : 50.88 MB
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An excellent introduction for all those coming to the subject for the first time. New material has been added to the second edition and the original six chapters have been modified. The previous edition sold 9500 copies world wide since its release in 1996. Based on numerous courses given by the author to students and researchers in the health sciences and is written with such readers in mind. Provides a "user-friendly" layout and includes numerous illustrations and exercises. Written in such a way so as to enable readers learn directly without the assistance of a classroom instructor. Throughout, there is an emphasis on presenting each new topic backed by real examples of a survival analysis investigation, followed up with thorough analyses of real data sets.
Category: Medical

Textbook Of Cancer Epidemiology

Author : Hans-Olov Adami
ISBN : 9780190676841
Genre : Medical
File Size : 79.51 MB
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"Comprehensive and comprehensible, but also encouraging -- informed by the hope and belief that informed its creation." -Cancer Amid sweeping advances in the science and treatment of cancer, the TEXTBOOK OF CANCER EPIDEMIOLOGY offers students and professionals a definitive, systematic resource for understanding the factors affecting all types of human cancer. This fully updated new edition offers an overview of epidemiology's key concepts and methods as they relate to cancer (including the emerging potential of biomarkers) as well as site-specific chapters on individual cancers' natural history, pathology, descriptive epidemiology, and etiology. Taken together, these chapters forge connections between established science and the ongoing evolution of this dynamic field. Crisply and concisely written by an assembly of internationally recognized researchers, the TEXTBOOK OF CANCER EPIDEMIOLOGY offers a superlative introduction to the subject's consensuses and controversies for those embarking on their careers and a ready reference for seasoned professionals.
Category: Medical

Adaptive Regression For Modeling Nonlinear Relationships

Author : George J. Knafl
ISBN : 9783319339467
Genre : Medical
File Size : 72.36 MB
Format : PDF
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This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the standard, logistic, and Poisson regression contexts with continuous, discrete, and counts outcomes, respectively, either univariate or multivariate. The book also provides a comparison of adaptive modeling to generalized additive modeling (GAM) and multiple adaptive regression splines (MARS) for univariate outcomes. The authors have created customized SAS macros for use in conducting adaptive regression modeling. These macros and code for conducting the analyses discussed in the book are available through the first author's website and online via the book’s Springer website. Detailed descriptions of how to use these macros and interpret their output appear throughout the book. These methods can be implemented using other programs.
Category: Medical

Modeling Survival Data Extending The Cox Model

Author : Terry M. Therneau
ISBN : 9781475732948
Genre : Mathematics
File Size : 59.6 MB
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This book is for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyze multiple/correlated event data using marginal and random effects. The focus is on actual data examples, the analysis and interpretation of results, and computation. The book shows how these new methods can be implemented in SAS and S-Plus, including computer code, worked examples, and data sets.
Category: Mathematics

An Introduction To Statistical Learning

Author : Gareth James
ISBN : 9781461471387
Genre : Mathematics
File Size : 47.30 MB
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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
Category: Mathematics

Applied Logistic Regression

Author : David W. Hosmer, Jr.
ISBN : 9780470582473
Genre : Mathematics
File Size : 28.24 MB
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"This Third Edition continues to focus on applications and interpretation of results from fitting regression models to categorical response variables"--
Category: Mathematics

Epidemiologic Research

Author : David G. Kleinbaum
ISBN : 047128985X
Genre : Medical
File Size : 85.45 MB
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Epidemiologic Research Principles and Quantitative Methods David G. Kleinbaum, Ph.D. Lawrence L. Kupper. Ph.D. Hal Morgenstern, Ph.D. Epidemiologic Research covers the principles and methods of planning, analysis and interpretation of epidemiologic research studies. It supplies the applied researcher with the most up-to-date methodological thought and practice. Specifically, the book focuses on quantitative (including statistical) issues arising from epidemiologic investigations, as well as on the questions of study design, measurement and validity. Epidemiologic Research emphasizes practical techniques, procedures and strategies. It presents them through a unified approach which follows the chronology of issues that arise during the investigation of an epidemic. The book’s viewpoint is multidisciplinary and equally useful to the epidemiologic researcher and to the biostatistician. Theory is supplemented by numerous examples, exercises and applications. Full solutions are given to all exercises in a separate solutions manual. Important features Thorough discussion of the methodology of epidemiologic research Stress on validity and hence on reliability Balanced approach, presenting the most important prevailing viewpoints Three chapters with applications of mathematical modeling
Category: Medical

Applied Longitudinal Data Analysis For Epidemiology

Author : Jos W. R. Twisk
ISBN : 9781107030039
Genre : Medical
File Size : 24.21 MB
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A practical guide to the most important techniques available for longitudinal data analysis, essential for non-statisticians and researchers.
Category: Medical

Handbook Of Statistical Analysis And Data Mining Applications

Author : Robert Nisbet
ISBN : 9780124166455
Genre : Mathematics
File Size : 65.5 MB
Format : PDF, Kindle
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Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications
Category: Mathematics