Book description
Pharmaceutical Statistics Using SAS: A Practical Guide offers extensive coverage of cutting-edge biostatistical methodology used in drug development and the practical problems facing today's drug developers. Written by well-known experts in the pharmaceutical industry Alex Dmitrienko, Christy Chuang-Stein, and Ralph D'Agostino, it provides relevant tutorial material and SAS examples to help readers new to a certain area of drug development quickly understand and learn popular data analysis methods and apply them to real-life problems. Step-by-step, the book introduces a wide range of data analysis problems encountered in drug development and illustrates them using a wealth of case studies from actual pre-clinical experiments and clinical studies. The book also provides SAS code for solving the problems. Among the topics addressed are these:
drug discovery experiments to identify promising chemical compounds
animal studies to assess the toxicological profile of these 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.
Additional features include a discussion of methodological issues, practical advice from subject-matter experts, and review of relevant regulatory guidelines. Most chapters are self-contained and include a fair amount of high-level introductory material to make them accessible to a broad audience of pharmaceutical scientists. This book will also serve as a useful reference for regulatory scientists as well as academic researchers and graduate students.
This book is part of the SAS Press program.
Table of contents
- Praise from the Experts
- Copyright
- Preface
- Statistics in Drug Development
- Modern Classification Methods for Drug Discovery
- Model Building Techniques in Drug Discovery
- Statistical Considerations in Analytical Method Validation
- Some Statistical Considerations in Nonclinical Safety Assessment
- Nonparametric Methods in Pharmaceutical Statistics
-
Optimal Design of Experiments in Pharmaceutical Applications
- Optimal Design problem
- Quantal Dose-Response Models
- Nonlinear Regression Models with a Continuous Response
- Regression Models with Unknown Parameters in the Variance Function
- Models with a Bounded Response (Beta Models
- Models with a Bounded Response (Logit Link)
- Bivariate Probit Models for Correlated Binary Responses
- Pharmacokinetic Models with Multiple Measurements per Patient
- Models with Cost Constraints
- Summary
- References
- Analysis of Human Pharmacokinetic Data
- Allocation in Randomized Clinical Trials
-
Sample-Size Analysis for Traditional Hypothesis Testing: Concepts and Issues
- Introduction
- Research Question 1: Does "QCA" Decrease Mortality in Children with Severe Malaria?
- p-Values, α, β and Power
- A Classical Power Analysis
- Beyond α and β: Crucial Type I and Type II Error Rates
- Research Question 1, Continued: Crucial Error Rates for Mortality Analysis
- Research Question 2: Does "QCA" Affect the "Elysemine: Elysemate" Ratios (EER)?
- Crucial Error Rates When the Null Hypothesis Is Likely to Be True
- Table of Crucial Error Rates
- Summary
- Acknowledgments
- References
- Appendix A Guidelines for "Statistical Considerations" Sections
- Appendix B SAS Macro Code to Automate the Programming
- Design and Analysis of Dose-Ranging Clinical Studies
- Analysis of Incomplete Data
- Reliability and Validity: Assessing the Psychometric Properties of Rating Scales
- Decision Analysis in Drug Development
Product information
- Title: Pharmaceutical Statistics Using SAS
- Author(s):
- Release date: February 2007
- Publisher(s): SAS Institute
- ISBN: 9781629590301
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