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If you find one, please contact us. | Chegg Select | $35.83 as of 11/21 1pm EST | Quarter Rental (85 days) | NO, $3.99 | There are no current coupons/deals for this store in our database. If you find one, please contact us. | Chegg Select | $38.49 as of 11/21 1pm EST | Semester Rental (125 days) | NO, $3.99 | There are no current coupons/deals for this store in our database. If you find one, please contact us. | Summaries and Customer Reviews are supplied by Amazon.com | This comprehensive guide bridges the gap between modern statistical methodology and real-world clinical trial applications. Step-by-step instructions illustrated with examples from actual trials and case studies serve to define a statistical method and its relevance in a clinical trials setting and to illustrate how to implement the method rapidly and efficiently using the power of SAS software. Topics reflect the International Conference on Harmonization (ICH) guidelines for the pharmaceutical industry and address important statistical problems encountered in clinical trials, including analysis of stratified data, incomplete data, multiple inferences, issues arising in safety and efficacy monitoring, and reference intervals for extreme safety and diagnostic measurements. Clinical statisticians, research scientists, and graduate students in biostatistics will greatly benefit from the decades of clinical research experience compiled in this book. Numerous ready-to-use SAS macros and example code are included. | Average Customer Rating: ideal book for those analyzing clinical trials I have previously reviewed a more recent book in the SAS series "Pharmaceutical Statistics using SAS: A Practical Guide" that Dmitrienko and Chuang-Stein also edited. Both books are very similar because they provide a variety of chapters by top experts in the field. Each chapter is well written and understandable to nonstatisticians who do clinical research as well as for statisticians who can use it as a reference and guide to the appropriate techniques to handle various common situations. I highly recommend this book to anyone who analyzes clinical trials data. Overly complicated language Good history of methods with lots of citations. Not so good for practical application. Programming language is not fully explained and results are difficult to understand and reproduce. Limited output shown. I want Ron Cody to write an "applied statistics for clinical trials" workbook. Comprehensive and thorough This is an excellent easy to follow book, from which both statisticians and interested medical professionals can greatly benefit, I believe, because I am working with both. It has helped me a lot in my graduate project on data analysis of laboratory animal trials. | |