In Analysis of Clinical Trials Using SAS: A Practical Guide, Alex Dmitrienko, Geert Molenberghs, Christy Chuang-Stein, and Walter Offen bridge 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. This book is part of the SAS Press program.

Analysis of Clinical Trials Using SAS: A Practical Guide

Computers & Technology

Molenberghs, Geert

Dmitrienko, Alex

Chuang-Stein, Christy

Offen, Walter

Software

SAS Institute

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: 
<ul>
<li>drug discovery experiments to identify promising chemical compounds </li>
<li>animal studies to assess the toxicological profile of these compounds </li>
<li>clinical pha</li>
</ul>

Pharmaceutical Statistics Using SAS: A Practical Guide

D'Agostino, Ralph

About the Author\nChristy Chuang-Stein received a bachelor degree in mathematics from the National Taiwan University and a PhD in statistics from the University of Minnesota. She retired from Pfizer as Vice President and Head of the Statistical Research and Consulting Center in July 2015, after 30 years in the pharmaceutical industry and 5 years in academia (University of Rochester). Currently, Christy is the owner and Principal Consultant of Chuang-Stein Consulting, LLC and consults broadly in the areas of pharmaceutical development and evaluation. 
Christy is a Fellow of the American Statistical Association (ASA) and received the ASA’s Founders’ Award in 2012. She was the recipient of the Distinguished Achievement Award of the International Chinese Statistical Association in 2013 and the Distinguished Service Award from the National Institute of Statistical Sciences in 2020. She is also a repeat recipient of the Drug Information Association’s Donald Francke Award for Excellence in Journal Publishing and the Thomas Teal Award for Excellence in Statistics Publishing. Christy is a founding editor of the journal Pharmaceutical Statistics. 
Simon Kirby received a BSc In Economics and Economic Policy from Loughborough University, an MSc in Statistics from the University of Kent, a PhD in Statistics from the University of Edinburgh and a BA in Mathematics from the Open University. He retired from Pfizer in 2018 after almost 20 years working as a Principal Statistician, Clinical Statistics Head, Therapeutic Area Statistics Head and Consultant in the Statistical Research and Consulting Center. He is the owner of SKSTATS Limited for which he does occasional statistical consultancy. 
Simon is a Fellow and Chartered Statistician of the Royal Statistical Society. He previously worked as a Lecturer, Senior Lecturer then Principal Lecturer in Statistics at Liverpool John Moores University and as a Statistician at the U.K.’s Institute of Food Research, Rothamsted Experimental Station and Revlon Healthcare.\nThis book focuses on important decision points and evidence needed for making decisions at these points during the development of a new drug. It takes a holistic approach towards drug development by incorporating explicitly knowledge learned from the earlier part of the development and available historical information into decisions at later stages. In addition, the book shares lessons learned from several select examples published in the literature since the publication of the first edition. 
The second edition reiterates the need for making evidence-based Go/No Go decisions in drug development discussed in the first edition. It substantially expands several topics that have seen great advances since the publication of the first edition. The most noticeable additions include three adaptive trials conducted in recent years that offer excellent learning opportunities, the use of historical data in the design and analysis of clinical trials, and extending decision criteria to the cases when the primary endpoint is binary. The examples used to illustrate the additional materials all come from real trials with some post-trial reflections offered by the authors. 
The book begins with an overview of product development and regulatory approval pathways. It then discusses how to incorporate prior knowledge into study design and decision making at different stages of drug development. Prior knowledge includes information pertaining to historical controls. To assist decision making, the book discusses appropriate metrics and the formulation of go/no-go decisions for progressing a drug candidate to the next development stage. Using the concept of the positive predictive value in the field of diagnostics, the book leads readers to the assessment of the probability that an investigational product is effective given positive study outcomes. Lastly, the book points out common mistakes made by drug developers under the current drug-devel

Quantitative Decisions in Drug Development (Springer Series in Pharmaceutical Statistics)

Science & Math

Kirby, Simon

Biological Sciences

Springer

This book offers a high-level treatise of evidence-based decisions in drug development. Because of the inseparable relationship between designs and decisions, a good portion of this book is devoted to the design of clinical trials. The book begins with an overview of product development and regulatory approval pathways. It then discusses how to&nbsp;incorporate prior knowledge into study design and decision making at different stages of drug development. The latter include selecting appropriate metrics to formulate decisions criteria,&nbsp;determining go/no-go decisions for progressing a drug candidate to the next stage and&nbsp;predicting the effectiveness of a product. Lastly, it points out common mistakes made by drug developers under the current drug-development paradigm. The book offers useful insights to statisticians, clinicians, regulatory affairs managers and decision-makers in the pharmaceutical industry who have a basic understanding of the drug-development process and the clinical trials conducted to support drug-marketing authorization. The authors provide software codes for select&nbsp;analytical approaches discussed in the book. The book includes enough technical details to allow statisticians to replicate the quantitative illustrations so that they can generate information to facilitate decision-making themselves.

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