Clinical Trial Data Analysis Using R and SAS (Chapman & Hall/CRC Biostatistics Series) - Tapa dura

Críticas

" . . . this book provides a very useful overview of the statistical methods used in the analysis of clinical trials, along with their implementations. This will particularly help clinical practitioners to apply these methodologies in their own scientific problems . . . I would really like to thank the authors, D. Chen, K. E. Peace and P. Zhang, for such a nice readymade reference for clinical trial analysis, with very interesting real data illustrations."
~Abhik Ghosh, International Society for Clinical Biostatistics

Reseña del editor

Review of the First Edition

"The goal of this book, as stated by the authors, is to fill the knowledge gap that exists between developed statistical methods and the applications of these methods. Overall, this book achieves the goal successfully and does a nice job. I would highly recommend it ...The example-based approach is easy to follow and makes the book a very helpful desktop reference for many biostatistics methods."
―Journal of Statistical Software

Clinical Trial Data Analysis Using R and SAS, Second Edition provides a thorough presentation of biostatistical analyses of clinical trial data with step-by-step implementations using R and SAS. The book’s practical, detailed approach draws on the authors’ 30 years’ experience in biostatistical research and clinical development. The authors develop step-by-step analysis code using appropriate R packages and functions and SAS PROCS, which enables readers to gain an understanding of the analysis methods and R and SAS implementation so that they can use these two popular software packages to analyze their own clinical trial data.

• Adds SAS programs along with the R programs for clinical trial data analysis.
• Updates all the statistical analysis with updated R packages.
• Includes correlated data analysis with multivariate analysis of variance.
• Applies R and SAS to clinical trial data from hypertension, duodenal ulcer, beta blockers, familial andenomatous polyposis, and breast cancer trials.
• Covers the biostatistical aspects of various clinical trials, including treatment comparisons, time-to-event endpoints, longitudinal clinical trials, and bioequivalence trials.