This essential book provides a general introduction to data mining and is filled with a variety of examples. While there are no "right" answers to investigating data sets, there are many questions that can be asked of the data. Each answer creates a path that will help you drill down to explore the data in more detail. It is up to the investigator to determine what is of interest and what is important to analyze, and this book provides excellent starting points.

Pharmaceutical Statistics will consist of 13 chapters motivated by data analysis problems arising at various stages of drug development: early drug discovery, animal toxicology, clinical pharmacology and other early human studies and, finally, clinical trials. This book will provide a lot of tutorial material and SAS examples to help professionals new to a certain area of drug development quickly understand and learn popular data analysis methods and apply them to real-life problems. The main goal of the book is to introduce a wide range of data analysis problems encountered in drug development and explain how to solve them in a real-time fashion, that is, describe relevant methodology, illustrate it using real-life examples, and provide SAS code for solving the problem. It will cover a broad range of topics including data analysis problems in drug discovery, toxicology, and pharmacology studies. It will address problems concerning the design and analysis of

• chemical experiments in drug discovery
• animal studies to assess the toxicological profile of chemical compounds
• clinical pharmacology studies to examine the properties of new drugs in healthy human subjects
• Phase II and Phase III clinical trials to establish therapeutic benefits of experimental drugs.