Statistical Programming For Clinical Trials in R
Preface
About the book
This is a book on statistical programming for clinical trials using the R programming language. Over the past decades SAS has been the dominant language in the pharmaceutical industry but things have been changing rapidly over the past few years and now there is a need of embracing open source languages like R and Python in the clinical programming pipeline.
This book focuses primarily on Human Clinical Trials which are experiments conducted in humans to assess safety and efficacy of drugs in the treatment of a particular indication (disease).
This book will teach you most of the things that you need to know to effectively analyze clinical trials data using R and point you to other relevant materials for further learning.
Currently the book has eight chapters and I might add more chapters in the future. The first two chapters will give you introductory information on clinical trials and the R programming language. If you are already working within the clinical trials domain and know how to use R to conduct data analysis, these two chapters can be skipped, but if you are new both to clinical trials and R please start with these two chapters and go through the suggested materials for further learning.
Chapters 3, 4 and 5 will teach you how to use R to actually carry out programming tasks relevant to clinical trials (Program SDTMs, ADAMS, and Outputs). You will need to consult a number of documents as you program, these documents include data programming specifications, annotated Case Report Forms, Protocol, Statistical Analysis Plan. Where feasible, I will try and provide samples for this key documents, if not provided I will explain what needs to be done in that particular scenario based on experience. Chapter 6 walks you through a sample of special topics that you might encounter in your clinical trials data analysis journey. These special topics relate to specific statistical methods commonly encountered in clinical data analysis, a good example is HTA (Health Technology Assessment) Analysis.
Chapter 7 is a bonus chapter that will help you prepare for interviews in the clinical domain.
Chapter 8 gives a summary of key takeaways in the book.
About the author
Eric is currently working as a Senior Statistical Programmer in the pharmaceutical industry. His experience spans across academia, banking sector and clinical industry. In the banking sector he worked as a Data Scientist and therefore he has exposure to both R and Python languages and how they can be used effectively to explore and model data in order to inform decision making.
He holds a Master’s Degree in Statistical Science from Strathmore University.