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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: Describes a system of principles for pragmatic problem solving in clinical drug development. Discusses differences in the work of a biostatistician in small pharma and big pharma. Explains the importance/relevance of statistical programming and data management for biostatistics and necessity for integration on various levels. Describes some useful statistical background that can be capitalized upon in the drug development enterprise. Explains some hot topics and current trends in biostatistics in simple, non-technical terms. Discusses incompleteness of any system of standard operating procedures, rules and regulations. Provides a classification of scoring systems and proposes a novel approach for evaluation of the safety outcome for a completed randomized clinical trial. Presents applications of the problem solving philosophy in a highly problematic transfusion field where many investigational compounds have failed. Discusses realistic planning of open-ended projects.

Mathematical and Statistical Skills in the Biopharmaceutical Industry: A Pragmatic Approach

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