Data Science for Business and Decision Making
Data Science for Business and Decision Making

Author: Luiz Paulo Favero

Publisher: Academic Press

Published: 2019-04-11

Total Pages: 1246

ISBN-13: 0128112174

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Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®. - Combines statistics and operations research modeling to teach the principles of business analytics - Written for students who want to apply statistics, optimization and multivariate modeling to gain competitive advantages in business - Shows how powerful software packages, such as SPSS and Stata, can create graphical and numerical outputs

Análisis de Estudios Clínicos en Oftalmología
Análisis de Estudios Clínicos en Oftalmología

Author: Juan Carlos Mesa Gutiérrez

Publisher: Lulu.com

Published: 2011-12-13

Total Pages: 448

ISBN-13: 1471625109

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Libro que describe cómo analizar y diseñar los diferentes tipos de investigaciones clínicas: ensayos clinicos aleatorizados, estudios observacionales, estudios diagnósticos, estudios pronósticos, estudios genéticos, análisis de evaluación económica, etc. Se proporcionan las herramientas estadísticas para el diseño, el análisis y la interpretación de los diferentes estudios clínicos en el campo de la investigación

Modeling Survival Data: Extending the Cox Model
Modeling Survival Data: Extending the Cox Model

Author: Terry M. Therneau

Publisher: Springer Science & Business Media

Published: 2013-11-11

Total Pages: 356

ISBN-13: 1475732945

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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.

Dynamic Prediction in Clinical Survival Analysis
Dynamic Prediction in Clinical Survival Analysis

Author: Hans van Houwelingen

Publisher: CRC Press

Published: 2011-11-09

Total Pages: 250

ISBN-13: 1439835438

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There is a huge amount of literature on statistical models for the prediction of survival after diagnosis of a wide range of diseases like cancer, cardiovascular disease, and chronic kidney disease. Current practice is to use prediction models based on the Cox proportional hazards model and to present those as static models for remaining lifetime a

Modelling Survival Data in Medical Research, Second Edition
Modelling Survival Data in Medical Research, Second Edition

Author: David Collett

Publisher: CRC Press

Published: 2003-03-28

Total Pages: 413

ISBN-13: 1584883251

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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.

Analysis of Survival Data
Analysis of Survival Data

Author: D.R. Cox

Publisher: Routledge

Published: 2018-02-19

Total Pages: 216

ISBN-13: 1351466607

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This monograph contains many ideas on the analysis of survival data to present a comprehensive account of the field. The value of survival analysis is not confined to medical statistics, where the benefit of the analysis of data on such factors as life expectancy and duration of periods of freedom from symptoms of a disease as related to a treatment applied individual histories and so on, is obvious. The techniques also find important applications in industrial life testing and a range of subjects from physics to econometrics. In the eleven chapters of the book the methods and applications of are discussed and illustrated by examples.