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Easy to read and comprehensive, Survival Analysis Using SAS: A Practical Guide, Second Edition, by Paul D. Allison, is an accessible, data-based introduction to methods of survival analysis. Researchers who want to analyze survival data with SAS will find just what they need with this fully updated new edition that incorporates the many enhancements in SAS procedures for survival analysis in SAS 9. Although the book assumes only a minimal knowledge of SAS, more experienced users will learn new techniques of data input and manipulation. Numerous examples of SAS code and output make this an eminently practical book, ensuring that even the uninitiated become sophisticated users of survival analysis. The main topics presented include censoring, survival curves, Kaplan-Meier estimation, accelerated failure time models, Cox regression models, and discrete-time analysis. Also included are topics not usually covered in survival analysis books, such as time-dependent covariates, competing risks, and repeated events. Survival Analysis Using SAS: A Practical Guide, Second Edition, has been thoroughly updated for SAS 9, and all figures are presented using ODS Graphics. This new edition also documents major enhancements to the STRATA statement in the LIFETEST procedure; includes a section on the PROBPLOT command, which offers graphical methods to evaluate the fit of each parametric regression model; introduces the new BAYES statement for both parametric and Cox models, which allows the user to do a Bayesian analysis using MCMC methods; demonstrates the use of the counting process syntax as an alternative method for handling time-dependent covariates; contains a section on cumulative incidence functions; and describes the use of the new GLIMMIX procedure to estimate random-effects models for discrete-time data. This book is part of the SAS Press program.
Survival analysis is a class of statistical methods for studying the occurrence and timing of events. These methods ar most often applied to the study of deaths. In fact, they were originally designed for that purpose, which explains the name survival analysis. That name is somewhat unfortunate, however, because it encourages a highly restricted view of the potential applications of these methods. Survival analysis is extremely useful for studying many different kinds of events in both the social and natural sciences, including the onset of disease, equipment failures, earthquakes, automobile accidents, stock market crahes, revoluations, job terminations, births, marriges, divorces, promotions, retirements and arrests. Because these methods have been adapted - and sometimes independently discovered - by researchers in several different fields, they also go by several different names: event history analysis (sociology), reliability analysis and failure time analysis (engineering), duration and transition analysis (u.a. developmental psychology, economics).
If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, Paul Allison's Logistic Regression Using SAS: Theory and Application, Second Edition, is for you! Informal and nontechnical, this book both explains the theory behind logistic regression, and looks at all the practical details involved in its implementation using SAS. Several real-world examples are included in full detail. This book also explains the differences and similarities among the many generalizations of the logistic regression model. The following topics are covered: binary logistic regression, logit analysis of contingency tables, multinomial logit analysis, ordered logit analysis, discrete-choice analysis, and Poisson regression. Other highlights include discussions on how to use the GENMOD procedure to do loglinear analysis and GEE estimation for longitudinal binary data. Only basic knowledge of the SAS DATA step is assumed. The second edition describes many new features of PROC LOGISTIC, including conditional logistic regression, exact logistic regression, generalized logit models, ROC curves, the ODDSRATIO statement (for analyzing interactions), and the EFFECTPLOT statement (for graphing nonlinear effects). Also new is coverage of PROC SURVEYLOGISTIC (for complex samples), PROC GLIMMIX (for generalized linear mixed models), PROC QLIM (for selection models and heterogeneous logit models), and PROC MDC (for advanced discrete choice models). This book is part of the SAS Press program.
In this follow-up to "Extending SAS Survival Analysis Techniques for Medical Research," the theory and methods of survival analysis and SAS procedures used to implement the methods are fully described. The new features, along with several useful macros and numerous examples, make this edition a suitable textbook for a course in survival analysis for biostatistics majors and majors in related fields.
Datenbanken sind das beste Werkzeug, um wichtige Informationen im Auge zu behalten. Mit SQL k?nnen die vorhandenen Daten strukturiert und zielsicher abgefragt werden. In "SQL f?r Dummies" macht Allen G. Taylor die Leser mit der Abfragesprache SQL und dem aktuellen Standard SQL:2011 verst?ndlich und humorvoll vertraut. Er beginnt mit dem notwendigen Grundlagenwissen f?r den Aufbau eines Datenbankmanagementsystems und den SQL-Hauptkomponenten, zeigt auf, wie man die Daten erstellt, ordnet und abfragt und SQL-Anweisungen in Programme einbindet. Zudem erkl?rt er, wie SQL mit XML verkn?pft werden kann, um Websites mit einer Datenbank zu verbinden. Au?erdem gibt es jede Menge Profi-Informationen, wie man zum Beispiel Datenbanken und Daten sch?tzen kann und Fehler lokalisiert und behebt.
Analysis of Clinical Trials Using SAS®: A Practical Guide, Second Edition bridges the gap between modern statistical methodology and real-world clinical trial applications. Tutorial material and step-by-step instructions illustrated with examples from actual trials serve to define relevant statistical approaches, describe their clinical trial applications, and implement the approaches rapidly and efficiently using the power of SAS. Topics reflect the International Conference on Harmonization (ICH) guidelines for the pharmaceutical industry and address important statistical problems encountered in clinical trials. Commonly used methods are covered, including dose-escalation and dose-finding methods that are applied in Phase I and Phase II clinical trials, as well as important trial designs and analysis strategies that are employed in Phase II and Phase III clinical trials, such as multiplicity adjustment, data monitoring, and methods for handling incomplete data. This book also features recommendations from clinical trial experts and a discussion of relevant regulatory guidelines. This new edition includes more examples and case studies, new approaches for addressing statistical problems, and the following new technological updates: SAS procedures used in group sequential trials (PROC SEQDESIGN and PROC SEQTEST) SAS procedures used in repeated measures analysis (PROC GLIMMIX and PROC GEE) macros for implementing a broad range of randomization-based methods in clinical trials, performing complex multiplicity adjustments, and investigating the design and analysis of early phase trials (Phase I dose-escalation trials and Phase II dose-finding trials) Clinical statisticians, research scientists, and graduate students in biostatistics will greatly benefit from the decades of clinical research experience and the ready-to-use SAS macros compiled in this book.

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