lt starts with God and tells about the world so that a small child would understand. It is written in rhythm, so parents can easily read it to children. It helps the child understand the world. What is special is that it gives a good, new type of relationship between God and Jesus Christ. It naturally gives deep religious ideas about the life of Jesus and how it all fits in his mission on earth, giving a human and divine portrayal of his life and allowing children to easily understand it.
lt starts with the idea that God saw a baby and wondered what it is to be a baby. He gets the plan of sending Christ to earth to be a baby and to grow up to be a man. Jesus then goes to earth and becomes a baby. For the plan to work, Jesus has to not know his divine nature and become just like any baby on earth.
This gives children a sense of the human nature of Jesus. Then when he is twelve, he gets a dream of his former divine nature, and when he is baptized, he gets to know his full divine nature.
He goes to the high hills and plans his future life to explain to people his divine mission. He gets twelve men to help him. Children naturally learn Jesus' human and divine natures. The last chapter makes children feel that Jesus is watching out for them and answering their prayers.
The approach taken in this book is to studies monitored over time, what the Central Limit Theorem is to studies with only one analysis. Just as the Central Limit Theorem shows that test statistics involving very different types of clinical trial outcomes are asymptotically normal, this book shows that the joint distribution of the test statistics at different analysis times is asymptotically multivariate normal with the correlation structure of Brownian motion ( the B-value ) irrespective of the test statistic. The so-called B-value approach to monitoring allows us to use, for different types of trials, the same boundaries and the same simple formula for computing conditional power. Although Brownian motion may sound complicated, the authors make the approach easy by starting with a simple example and building on it, one piece at a time, ultimately showing that Brownian motion works for many different types of clinical trials.
The book will be very valuable to statisticians involved in clinical trials. The main body of the chapters is accessible to anyone with knowledge of a standard mathematical statistics text. More mathematically advanced readers will find rigorous developments in appendices at the end of chapters. Reading the book will develop insight into not only monitoring, but power, survival analysis, safety, and other statistical issues germane to clinical trials.
Michael Proschan, Gordon Lan, and Janet Wittes are elected Fellows of the American Statistical Association. All have spent formative years in the Biostatistics Research Branch of the National Heart, Lung, and Blood Institute (NHLBI/NIH). While there, they were intimately involved in the design and statistical monitoring of large-scale randomized clinical trials, developing methodology to aid in their monitoring. For example, Lan developed, with DeMets, the now widely-used spending function approach to group sequential designs, whose properties were further investigated by Proschan. The B-value approach used in the book was introduced in a very influential paper by Lan and Wittes. The statistical theory behind conditional power was developed by Lan, along with Simon and Halperin, and was the cornerstone for the conditional error approach to adaptive clinical trials introduced by Proschan and Hunsberger. All three authors have expertise in adaptive methodology for clinical trials.
Michael Proschan is a Mathematical Statistician at the National Institutes of Health; Gordon Lan is Senior Director of Biometrics at Johnson & Johnson Pharmaceutical Research and Development, L.L.C.; Janet Wittes is President of Statistics Collaborative, a statistical consulting company she founded in 1990."
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