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Clinical Statistics: Introducing Clinical Trials, Survival Analysis, and Longitudinal Data Analysis provides the mathematic background necessary for students preparing for a career as a statistician in the biomedical field. The manual explains the steps a clinical statistician must take in clinical trials from protocol writing to subject randomization, to data monitoring, and on to writing a final report to the FDA. All of the necessary fundamentals of statistical analysis: survival and longitudinal data analysis are included. SAS procedures are explained with simple examples and the mathematics behind these SAS procedures are covered in detail with the statistical software program SAS which is implemented throughout the text. Complete codes are given for every example found in the text. The exercises featured throughout the guide are both theoretical and applied making it appropriate for those moving on to different clinical settings. Students will find Clinical Statistics to be a handy lab reference for coursework and in their future careers.

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Clearly and concisely, this volume explains the entire book publishing process. Written by the editorial staff at Sage Publications Inc, it explains how to prepare an effective book proposal, how to respond to reviews and how to secure a contract. Detailed coverage is provided of contracts, book production and promotion. Throughout, publishing is presented not only as a business but also as a partnership between author and publisher.

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Buy Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health)

Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health) by Springer




Here is a unified, readable introduction to multipredictor regression methods in biostatistics, including linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, and generalized linear models for counts and other outcomes. The authors describe shared elements in methods for selecting, estimating, checking, and interpreting each model, and show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.Read more Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health)

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Survival Analysis for Epidemiologic and Medical Research (Practical Guides to Biostatistics and Epidemiology)

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This practical guide shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to understanding the analysis of many kinds of data. Similarly, discussions of such statistical concepts such as bias, confounding, independence, and interaction are presented in the context of survival analysis as well as the basic components of a broad range of applications.

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Survival Analysis: Techniques for Censored and Truncated Data (Statistics for Biology and Health) Description

Applied statisticians in many fields must frequently analyze time to event data. While the statistical tools presented in this book are applicable to data from medicine, biology, public health, epidemiology, engineering, economics, and demography, the focus here is on applications of the techniques to biology and medicine. The analysis of survival experiments is complicated by issues of censoring, where an individual's life length is known to occur only in a certain period of time, and by truncation, where individuals enter the study only if they survive a sufficient length of time or individuals are included in the study only if the event has occurred by a given date. The use of counting process methodology has allowed for substantial advances in the statistical theory to account for censoring and truncation in survival experiments. This book makes these complex methods more accessible to applied researchers without an advanced mathematical background. The authors present the essence of these techniques, as well as classical techniques not based on counting processes, and apply them to data. Practical suggestions for implementing the various methods are set off in a series of Practical Notes at the end of each section. Technical details of the derivation of the techniques are sketched in a series of Technical Notes. This book will be useful for investigators who need to analyze censored or truncated life time data, and as a textbook for a graduate course in survival analysis. The prerequisite is a standard course in statistical methodology.

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THE MOST PRACTICAL, UP-TO-DATE GUIDE TO MODELLING AND ANALYZING TIME-TO-EVENT DATA—NOW IN A VALUABLE NEW EDITION

Since publication of the first edition nearly a decade ago, analyses using time-to-event methods have increase considerably in all areas of scientific inquiry mainly as a result of model-building methods available in modern statistical software packages. However, there has been minimal coverage in the available literature to9 guide researchers, practitioners, and students who wish to apply these methods to health-related areas of study. Applied Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical, epidemiological, biostatistical, and other health-related research.

This book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. It offers a clear and accessible presentation of modern modeling techniques supplemented with real-world examples and case studies. Key topics covered include: variable selection, identification of the scale of continuous covariates, the role of interactions in the model, assessment of fit and model assumptions, regression diagnostics, recurrent event models, frailty models, additive models, competing risk models, and missing data.

Features of the Second Edition include:

• Expanded coverage of interactions and the covariate-adjusted survival functions
• The use of the Worchester Heart Attack Study as the main modeling data set for illustrating discussed concepts and techniques
• New discussion of variable selection with multivariable fractional polynomials
• Further exploration of time-varying covariates, complex with examples
• Additional treatment of the exponential, Weibull, and log-logistic parametric regression models
• Increased emphasis on interpreting and using results as well as utilizing multiple imputation methods to analyze data with missing values
• New examples and exercises at the end of each chapter

Analyses throughout the text are performed using Stata® Version 9, and an accompanying FTP site contains the data sets used in the book. Applied Survival Analysis, Second Edition is an ideal book for graduate-level courses in biostatistics, statistics, and epidemiologic methods. It also serves as a valuable reference for practitioners and researchers in any health-related field or for professionals in insurance and government.

Best Analysis of Multivariate Survival Data (Statistics for Biology and Health)

Analysis of Multivariate Survival Data (Statistics for Biology and Health) by Springer




Survival data or more general time-to-event data occur in many areas, including medicine, biology, engineering, economics, and demography, but previously standard methods have requested that all time variables are univariate and independent. This book extends the field by allowing for multivariate times. As the field is rather new, the concepts and the possible types of data are described in detail. Four different approaches to the analysis of such data are presented from an applied point of view.Read more Analysis of Multivariate Survival Data (Statistics for Biology and Health)

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Was diabetes evolution's response to the last Ice Age? Did a deadly genetic disease help our ancestors survive the bubonic plagues of Europe? Will a visit to the tanning salon help lower your cholesterol? Why do we age? Why are some people immune to HIV? Can your genes be turned on -- or off?

Joining the ranks of modern myth busters, Dr. Sharon Moalem turns our current understanding of illness on its head and challenges us to fundamentally change the way we think about our bodies, our health, and our relationship to just about every other living thing on earth, from plants and animals to insects and bacteria.

Through a fresh and engaging examination of our evolutionary history, Dr. Moalem reveals how many of the conditions that are diseases today actually gave our ancestors a leg up in the survival sweepstakes. When the option is a long life with a disease or a short one without it, evolution opts for disease almost every time.

Everything from the climate our ancestors lived in to the crops they planted and ate to their beverage of choice can be seen in our genetic inheritance. But Survival of the Sickest doesn't stop there. It goes on to demonstrate just how little modern medicine really understands about human health, and offers a new way of thinking that can help all of us live longer, healthier lives.

Survival of the Sickest is filled with fascinating insights and cutting-edge research, presented in a way that is both accessible and utterly absorbing. This is a book about the interconnectedness of all life on earth -- and, especially, what that means for us.

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Product Description : Survival of the Sickest: A Medical Maverick Discovers Why We Need Disease

Subject: Research

Subject: Human evolution

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Subject: Life Sciences - Evolution - Human

Subject: General Medical

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Critically acclaimed and resoundingly popular in its first edition, Modelling Survival Data in Medical Research has been thoroughly revised and updated to reflect the many developments and advances--particularly in software--made in the field over the last 10 years. Now, more than ever, it provides an outstanding text for upper-level and graduate courses in survival analysis, biostatistics, and time-to-event analysis.The treatment begins with an introduction to survival analysis and a description of four studies that lead to survival data. Subsequent chapters then use those data sets and others to illustrate the various analytical techniques applicable to such data, including the Cox regression model, the Weibull proportional hazards model, and others. This edition features a more detailed treatment of topics such as parametric models, accelerated failure time models, and analysis of interval-censored data. The author also focuses the software section on the use of SAS, summarising the methods used by the software to generate its output and examining that output in detail. Profusely illustrated with examples and written in the author's trademark, easy-to-follow style, Modelling Survival Data in Medical Research, Second Edition is a thorough, practical guide to survival analysis that reflects current statistical practices.

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Main Features : Statistical Methods for Survival Data Analysis

Third Edition brings the text up to date with new material and updated references.

• New content includes an introduction to left and interval censored data; the log-logistic distribution; estimation procedures for left and interval censored data; parametric methods iwth covariates; Cox's proportional hazards model (including stratification and time-dependent covariates); and multiple responses to the logistic regression model.
• Coverage of graphical methods has been deleted.
• Large data sets are provided on an FTP site for readers' convenience.
• Bibliographic remarks conclude each chapter.

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Data collected on the time to an event-such as the death of a patient in a medical study-is known as survival data. The methods for analyzing survival data can also be used to analyze data on the time to events such as the recurrence of a disease or relief from symptoms. Modelling Survival Data in Medical Research begins with an introduction to survival analysis and a description of four studies in which survival data was obtained. These and other data sets are then used to illustrate the techniques presented in the following chapters, including the Cox and Weibull proportional hazards models; accelerated failure time models; models with time-dependent variables; interval-censored survival data; model checking; and use of statistical packages. Designed for statisticians in the pharmaceutical industry and medical research institutes, and for numerate scientists and clinicians analyzing their own data sets, this book also meets the need for an intermediate text which emphasizes the application of the methodology to survival data arising from medical studies.

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"An excellent introduction to using SPSS for data analysis...extremely useful for undergraduate students, and covers a good range of material often not found in competing texts. It provides a self-contained resource itself, with more than simply (detailed and clear) step-by-step descriptions of statistical procedures in SPSS. There is also a wealth of tips and advice, and for each statistical technique a brief, but consistently reliable, explanation is provided." - George Dunbar, University of Warwick

"This book is an excellent addition to the research methods literature. It presents the research process, research strategy and SPSS techniques in manageable steps offering clear advice, useful tips and discussion of relevant issues such as assumptions and effect size...this text is written in an encouraging and supportive style. I believe students will read and learn with this book." - David Cairns, Macquarie University, Australia

The SPSS Survival Manual throws a lifeline to students and researchers grappling with SPSS. Written in a friendly, jargon-free style, it demystifies statistics and data analysis by guiding you through the entire research process and helping you to choose the right statistical technique for your project. From the formulation of research questions, to the design of the study and analysis of data, to reporting the results, Julie Pallant discusses basic and advanced statistical techniques. She outlines each technique clearly, with step-by-step procedures for performing the analysis, a detailed guide to interpreting SPSS output and an example of how to present the results in a report. The user-friendliness of the manual is enhanced by spiral binding which makes it easy to use at a computer. A recommended reading section points the reader towards additional sources of advice. Illustrated with screen grabs, examples of output and tips, and supported by a website (www.openup.co.uk/spss) with sample data and guidelines on report writing, the SPSS Survival Manual can be used by students and researchers at any level alongside any major statistics textbook.

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Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health) by Springer




This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes. Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way. The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided.Read more Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health)

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