Michael Proschan is a mathematical statistician and Fellow of the American Statistical Association with 32 years of clinical trial experience in cardiovascular and infectious diseases, including HIV/AIDS, Ebola virus disease, and COVID-19. He has expertise in statistical monitoring of clinical trials, having taught short courses and co-authored the book Statistical Monitoring of Clinical Trials: A Unified Approach with Gordon Lan and Janet Wittes. He co-authored, with Sally Hunsberger, one of the first papers on adaptive clinical trial methods using the observed treatment effect at an interim analysis. More recently, Dr. Proschan has written about the vital role re-randomization tests play in adaptive methods before breaking the treatment blind. He has recently been an adjunct faculty member at George Washington University and Johns Hopkins University’s Advanced Academic Programs.

"The goal of the book Statistical Thinking in Clinical Trials is to engender statistical intuition that can be used in different types of clinical trials, and author Michael A. Proschan brilliantly achieved the goal. The biggest part of the book is dedicated to re-randomization tests, which can be applied in a multitude of settings. The author shows their beauty and simplicity. To illustrate theoretical explanations, the author uses examples of real trials. There are not many examples of trials, but the author returns to them in different chapters and at the end of studying the book, readers become familiar with these examples. I think, it is a positive aspect of the book and provides a better understanding of the practical application of the techniques described in the book than the use of many different examples during the presentation of the material. It should be noted that author uses examples not only to show the correct methods used in trials, but also to show possible mistakes that may occur during the trial and how to avoid them. [...] I would recommend [this book] for biostatisticians who not only intent on deeper learning statistical techniques but are also interested in improvement of statistical intuition and using it in different types of clinical trials."-Maria Ivanchuk, in ISCB News, June 2022

• 1. Evidence and Inference. 2. 2 × 2 Tables. 3. Introduction to Clinical Trials. 4. Design of Clinical Trials. 5. Randomization/Allocation. 6. Randomization-Based Inference7 Survival Analysis. 7. Sample Size/Power. 8. Monitoring. 9. M&Ms: Multiplicity & Missing Data. 10. Adaptive Methods.

Michael Proschan is a mathematical statistician and Fellow of the American Statistical Association with 32 years of clinical trial experience in cardiovascular and infectious diseases, including HIV/AIDS, Ebola virus disease, and COVID-19. He has expertise in statistical monitoring of clinical trials, having taught short courses and co-authored the book Statistical Monitoring of Clinical Trials: A Unified Approach with Gordon Lan and Janet Wittes. He co-authored, with Sally Hunsberger, one of the first papers on adaptive clinical trial methods using the observed treatment effect at an interim analysis. More recently, Dr. Proschan has written about the vital role re-randomization tests play in adaptive methods before breaking the treatment blind. He has recently been an adjunct faculty member at George Washington University and Johns Hopkins University’s Advanced Academic Programs.

"The goal of the book Statistical Thinking in Clinical Trials is to engender statistical intuition that can be used in different types of clinical trials, and author Michael A. Proschan brilliantly achieved the goal. The biggest part of the book is dedicated to re-randomization tests, which can be applied in a multitude of settings. The author shows their beauty and simplicity. To illustrate theoretical explanations, the author uses examples of real trials. There are not many examples of trials, but the author returns to them in different chapters and at the end of studying the book, readers become familiar with these examples. I think, it is a positive aspect of the book and provides a better understanding of the practical application of the techniques described in the book than the use of many different examples during the presentation of the material. It should be noted that author uses examples not only to show the correct methods used in trials, but also to show possible mistakes that may occur during the trial and how to avoid them. [...] I would recommend [this book] for biostatisticians who not only intent on deeper learning statistical techniques but are also interested in improvement of statistical intuition and using it in different types of clinical trials."-Maria Ivanchuk, in ISCB News, June 2022

• 1. Evidence and Inference. 2. 2 × 2 Tables. 3. Introduction to Clinical Trials. 4. Design of Clinical Trials. 5. Randomization/Allocation. 6. Randomization-Based Inference7 Survival Analysis. 7. Sample Size/Power. 8. Monitoring. 9. M&Ms: Multiplicity & Missing Data. 10. Adaptive Methods.

Michael Proschan is a mathematical statistician and Fellow of the American Statistical Association with 32 years of clinical trial experience in cardiovascular and infectious diseases, including HIV/AIDS, Ebola virus disease, and COVID-19. He has expertise in statistical monitoring of clinical trials, having taught short courses and co-authored the book Statistical Monitoring of Clinical Trials: A Unified Approach with Gordon Lan and Janet Wittes. He co-authored, with Sally Hunsberger, one of the first papers on adaptive clinical trial methods using the observed treatment effect at an interim analysis. More recently, Dr. Proschan has written about the vital role re-randomization tests play in adaptive methods before breaking the treatment blind. He has recently been an adjunct faculty member at George Washington University and Johns Hopkins University’s Advanced Academic Programs.

"The goal of the book Statistical Thinking in Clinical Trials is to engender statistical intuition that can be used in different types of clinical trials, and author Michael A. Proschan brilliantly achieved the goal. The biggest part of the book is dedicated to re-randomization tests, which can be applied in a multitude of settings. The author shows their beauty and simplicity. To illustrate theoretical explanations, the author uses examples of real trials. There are not many examples of trials, but the author returns to them in different chapters and at the end of studying the book, readers become familiar with these examples. I think, it is a positive aspect of the book and provides a better understanding of the practical application of the techniques described in the book than the use of many different examples during the presentation of the material. It should be noted that author uses examples not only to show the correct methods used in trials, but also to show possible mistakes that may occur during the trial and how to avoid them. [...] I would recommend [this book] for biostatisticians who not only intent on deeper learning statistical techniques but are also interested in improvement of statistical intuition and using it in different types of clinical trials."-Maria Ivanchuk, in ISCB News, June 2022

• 1. Evidence and Inference. 2. 2 × 2 Tables. 3. Introduction to Clinical Trials. 4. Design of Clinical Trials. 5. Randomization/Allocation. 6. Randomization-Based Inference7 Survival Analysis. 7. Sample Size/Power. 8. Monitoring. 9. M&Ms: Multiplicity & Missing Data. 10. Adaptive Methods.