Statistical Thinking in Clinical Trials

AvMichael A. Proschan

Inbunden, Engelska, 2021

Del i serien Chapman & Hall/CRC Biostatistics Series

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Beskrivning

Statistical Thinking in Clinical Trials combines a relatively small number of key statistical principles and several instructive clinical trials to gently guide the reader through the statistical thinking needed in clinical trials. Randomization is the cornerstone of clinical trials and randomization-based inference is the cornerstone of this book. Read this book to learn the elegance and simplicity of re-randomization tests as the basis for statistical inference (the analyze as you randomize principle) and see how re-randomization tests can save a trial that required an unplanned, mid-course design change. Other principles enable the reader to quickly and confidently check calculations without relying on computer programs. The `EZ’ principle says that a single sample size formula can be applied to a multitude of statistical tests. The `O minus E except after V’ principle provides a simple estimator of the log odds ratio that is ideally suited for stratified analysis with a binary outcome. The same principle can be used to estimate the log hazard ratio and facilitate stratified analysis in a survival setting. Learn these and other simple techniques that will make you an invaluable clinical trial statistician.

Produktinformation

Utgivningsdatum:2021-11-24
Mått:178 x 254 x 24 mm
Vikt:650 g
Format:Inbunden
Språk:Engelska
Serie:Chapman & Hall/CRC Biostatistics Series
Antal sidor:264
Förlag:Taylor & Francis Ltd
ISBN:9781138058590

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Mer om författaren

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.

Recensioner i media

"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

Innehållsförteckning

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.