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Chapman and Hall/CRC
227 pages
Mathematical and Statistical Skills in the Biopharmaceutical Industry: A Pragmatic Approach describes a philosophy of efficient problem solving showcased using examples pertinent to the biostatistics function in clinical drug development. It was written to share a quintessence of the authors’ experiences acquired during many years of relevant work in the biopharmaceutical industry. The book will be useful will be useful for biopharmaceutical industry statisticians at different seniority levels and for graduate students who consider a biostatistics-related career in this industry.
Features:
"This is a highly original, extremely interesting, and in many ways impressive book. Its matter is largely about what it entails to be a professional biostatistician (in the widest sense of the word): what skills and attitudes you need to solve the problems encountered in exercising your profession. It is clear that the authors have thought long and hard about the problems they present. The case-studies are real, complex, interesting, and instructive. The book could only have been written by skilled scientists with long experience of working in the pharmaceutical industry. The reader benefits from the authors’ experience. The reader can be in no doubt that he or she is in the hands of experts. I learned a lot from the book." ~Stephen Senn, Retired, Honorary Professor, University of Sheffield
1. Background and Motivation
Pragmatic approach to problem solving
Problem solving skills
Mathematics versus statistics
A look at the modern drug development
Stages of drug development
Factors that have had an impact on drug development
Statistics and evidence-based science
In summary: what this book is all about
Introduction to Chapters 2, 3, and 4
2 Statistical Programming
Introduction
Asking the right questions
Choice of statistical and presentation software
\95/5" rule
The sources
SAS Certification | Is it worth time and efforts?
Data access, data creation and data storage
Getting data from external files
Data handling
The DATA step
Loops and arrays
Going from vertical to horizontal datasets and vice versa
Why do we need basic knowledge of the Macro language?
Open code vs. DATA step
Loops in the open code (inside macros) and nested macros
Use of pre-written (by others) macro code
Summary
3 Data Management
Introduction
Design of data collection
Organization of data collection
Data cleaning or verification
Re-structuring of the data
First case study
Second case study
Summary
4 Biostatistics
Introduction
The biostatistician's role
Background assessment: what do we start with?
A minimal sufficient set of tools for the biostatistician
Knowledge of the disease area
Knowledge of the regulatory landscape
Understanding of the clinical trial protocol
Knowledge of statistical methodologies for protocol development
Statistical software
Communication skills
Knowledge of processes
Advanced biostatistics toolkit
Adaptive designs
Basket, umbrella, platform trials and master protocols
Dose-finding methods
Multiplicity issues
Estimands
Quantitative decision-making support
Digital development
Summary
Introduction to Chapters 5, 6, and 7
5 Development of New Validated Scoring Systems
Introduction
Recognition of problem existence
Study of available methods and tools with consequent realization that they are insufficient
Clear formulation and formalization of the main task to be solved
A solution itself
Are we finished? Not in the regulatory setting!
Assessment of created by-products as potentially new tools, skills and methods
Generalization of all achievements and evaluation of potential applications in real world
6 Resurrecting a Failed Clinical Program
Preamble: what we are dealing with
Problems solved
Studying drugs with dosage that depends on needs
Separation of toxicity and efficacy effects in safety outcome misbalance
Creation of a PK model for the transfusion field
Mystery of the transfusion trigger
The rise and fall of the HBOC field
Summary
7 Can One Predict Unpredictable?
Personal disclaimer/preamble
First, what can we do?
Problems in planning of the open-ended projects
Extraneous vs. overlooked parts in preliminary planning
Level of uncertainty of elementary tasks
Terminology and definitions
Estimating distribution of time to completion of an open-ended project
Surprising results of first test runs of the algorithm
The nature of estimates for elementary tasks
Estimation for a single branch
How to analyze the results?
Summary
Appendix A: Relativistic and Probabilistic Functions
Appendix B: Manual for Successful Crusade in Defence of Patients' Rights
Afterword
Final Remark
Bibliography
