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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 & Development, L.L.C.; Janet Wittes is President of Statistics Collaborative, a statistical consulting company she founded in 1990.

Clinical trials --- Drugs --- Statistical methods. --- Testing. --- Clinical drug trials --- Clinical trials of drugs --- Drug bioscreening --- Drug trials --- Clinical pharmacology --- Drug development --- Effectiveness --- Testing --- Evaluation --- Statistics. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Statistics .

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Die Anwendung statistischer Methoden wird heute in der Regel durch den Einsatz von Computern unterstützt. Das frei verfügbare Programm R ist dabei ein leicht erlernbares und flexibel einzusetzendes Werkzeug, mit dem der Prozess der Datenanalyse nachvollziehbar verstanden und gestaltet werden kann. Die Anwendung und der Nutzen des Programms werden in diesem Buch anhand zahlreicher mit R durchgerechneter Beispiele veranschaulicht. Es erläutert statistische Ansätze und gibt leicht fasslich, anschaulich und praxisnah Studenten, Dozenten und Praktikern die notwendigen Details, um Daten zu gewinnen, zu beschreiben und zu beurteilen. Es dient zum Lernen, Nachschlagen und Anwenden bei unterschiedlichen Vorkenntnissen und breit gestreuten Interessen in der Hochschule und in der Praxis. Neben Hinweisen und Empfehlungen zur Planung und Auswertung von Studien ermöglichen zahlreiche Beispiele, Querverweise und ein ausführliches Sachverzeichnis einen gezielten Zugang zur Statistik, insbesondere für Mediziner, Ingenieure und Naturwissenschaftler. Eine noch klarere Gliederung, der Einsatz statistischer Software und insbesondere neue und aktuelle Ansätze bei Verteilungsmodellen und statistischen Verfahren sind die Hauptmerkmale dieser zwölften, vollständig neu bearbeiteten Auflage.

Statistics. --- Statistical Theory and Methods. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences. --- Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. --- Mathematical statistics. --- Statistique --- Statistique mathématique --- Mathematics. --- Probabilities. --- Probability Theory and Stochastic Processes. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Distribution (Probability theory. --- Statistics for Social Sciences, Humanities, Law. --- Statistics .

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The process of discovery in science and technology may require investigation of a large number of features, such as factors, genes or molecules. In Screening, statistically designed experiments and analyses of the resulting data sets are used to identify efficiently the few features that determine key properties of the system under study. This book brings together accounts by leading international experts that are essential reading for those working in fields such as industrial quality improvement, engineering research and development, genetic and medical screening, drug discovery, and computer simulation of manufacturing systems or economic models. Our aim is to promote cross-fertilization of ideas and methods through detailed explanations, a variety of examples and extensive references. Topics cover both physical and computer simulated experiments. They include screening methods for detecting factors that affect the value of a response or its variability, and for choosing between various different response models. Screening for disease in blood samples, for genes linked to a disease and for new compounds in the search for effective drugs are also described. Statistical techniques include Bayesian and frequentist methods of data analysis, algorithmic methods for both the design and analysis of experiments, and the construction of fractional factorial designs and orthogonal arrays. The material is accessible to graduate and research statisticians, and to engineers and chemists with a working knowledge of statistical ideas and techniques. It will be of interest to practitioners and researchers who wish to learn about useful methodologies from within their own area as well as methodologies that can be translated from one area to another. Angela Dean is Professor of Statistics at The Ohio State University, USA. She is a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. Her research focuses on the construction of efficient designs for factorial experiments in industry and marketing. She is co-author of the textbook Design and Analysis of Experiments and has served on the editorial boards of the Journal of the Royal Statistical Society and Technometrics. Susan Lewis is a Professor of Statistics at the University of Southampton, UK, and Deputy Director of the Southampton Statistical Sciences Research Institute. She has research interests in screening, design algorithms and the design and analysis of experiments in industry. She was awarded the Greenfield Industrial Medal by the Royal Statistical Society in 2005. She has served the Society as a Vice-President and a Member of Council, as well as a former Editor of the Journal of the Royal Statistical Society, Series C (Applied Statistics).

Medicine --- Medical screening. --- Research --- Statistical methods. --- Mass medical screening --- Mass screening, Medical --- Medical examinations --- Screening, Medical --- Diagnostic services --- Health risk assessment --- Mathematical statistics --- Mathematical statistics. --- Statistics. --- Statistical Theory and Methods. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistics .

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Survival analysis, the analysis of failure time data, is a rapid developing area and a number of books on the topic have been published in last twenty-five years. However, all of these books deal with right-censored failure time data, not the analysis of interval-censored failure time data. Interval-censored data include right-censored data as a special case and occur in many fields. The analysis of interval-censored data is much more difficult than that of right-censored data because the censoring mechanism that yields interval censoring is more complicated than that for right censoring. This book collects and unifies statistical models and methods that have been proposed for analyzing interval-censored failure time data. It provides the first comprehensive coverage of the topic of interval-censored data and complements the books on right-censored data. A number of inference approaches are discussed in the book, including the maximum likelihood, estimating equations, sieve maximum likelihood, and conditional likelihood. One major difference between the analyses of right- and interval-censored data is that the theory of counting processes, which is responsible for substantial advances in the theory and development of modern statistical methods for right-censored data, is not applicable to interval-censored data. The focus of the book is on nonparametric and semiparametric inferences, but it also describes parametric and imputation approaches. In addition, Bayesian methods and the analysis of interval-censored data with informative interval censoring are considered as well as the analysis of interval-censored recurrent event, or panel count, data. This book provides an up-to-date reference for people who are conducting research on the analysis of interval-censored failure time data as well as for those who need to analyze interval-censored data to answer substantive questions. It can also be used as a text for a graduate course in statistics or biostatistics that assume a basic knowledge of probability and statistics. Jianguo (Tony) Sun is a professor at the Department of Statistics of the University of Missouri-Columbia. He has developed novel statistical methods for the analysis of interval-censored failure time data and panel count data over the last fifteen years.

Failure time data analysis. --- Analysis, Failure time data --- Data analysis, Failure time --- Failure analysis (Engineering) --- Mathematical statistics --- Competing risks --- Survival analysis (Biometry) --- Distribution (Probability theory. --- Statistics. --- Probability Theory and Stochastic Processes. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Probabilities. --- Statistics . --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Risk

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Pattern-Based Compression of Multi-Band Image Data for Landscape Analysis describes an integrated approach to using remotely sensed data in conjunction with geographic information systems (GIS) for landscape analysis. Remotely sensed data are compressed by compound segmentation so that the first level is an image-like raster map for GIS, and a second level affords approximate restoration. Pattern processing is implemented in software by PSIMAPP Progressively Segmented Image Modeling As Poly-Patterns. There are seven notable areas of advantage in this approach: Controlling and creating contrast for pictorial presentations. Classifying content for constructing categorical maps. Whereas digital image data are usually directed toward algorithmic assignment of image elements to candidate categories of content, this approach is equally applicable to assisting interactive interpretive assignment by a human analyst. Detecting difference between instances of imaging. Whereas conventional change detection is done in the signal domain, this approach supports dual pattern matching in signal and spatial domains. Advantage in contextual considerations. Having parsed patterns into collective components allows analysts to conduct comparatives in multiple modes. The components can be combined according to signal similarities and proximate positioning to generate generalized images that portray progressively more prominent patterning. The patterns can be treated as multivariate trends for removal to reach residuals that are regionalized in accordance with scenarios of spatial statistics. An entirely new arena of analysis is posed by pattern profiles of cumulated components over blocks at several scales. Compositional components of complexes can be considered in terms of chromaticity or ratio relations among signal sets by partial ordering and rank range runs. Informational compression for conveyance by computer media. The poly-pattern models occupy the equivalent of two single-byte signal bands along with tables of pattern properties. Although approximation in restoration might appear to be a drawback, it leads to the sixth aspect of advantage. Digital image data are often proprietary with strictures on distribution. Since the poly-pattern models do not provide capability for complete restoration, and in view of their numerous advantages, they become substantially different derivative products in much the same manner as a thematic map. Therefore, most of the proprietary concerns relative to the original data should be obviated. The interface between image analysis and GIS. GIS provides the popular platform for utilization of geo-spatial information. Since relatively few of the regular GIS users are image analysts, poly-pattern packaging facilitates broader access to image-based information.

Landscape ecology --- Remote sensing. --- Research --- Statistical methods. --- Data processing. --- Ecology --- Environmental sciences. --- Landscape ecology. --- Statistics. --- Environment, general. --- Remote Sensing/Photogrammetry. --- Landscape Ecology. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Environmental science --- Science --- Environment. --- Statistics . --- Remote-sensing imagery --- Remote sensing systems --- Remote terrain sensing --- Sensing, Remote --- Terrain sensing, Remote --- Aerial photogrammetry --- Aerospace telemetry --- Detectors --- Space optics --- Balance of nature --- Biology --- Bionomics --- Ecological processes --- Ecological science --- Ecological sciences --- Environment --- Environmental biology --- Oecology --- Environmental sciences --- Population biology