Analysis of Clinical Trials Using SAS®: A Practical Guide, Second Edition bridges the gap between modern statistical methodology and real-world clinical trial applications. Tutorial material and step-by-step instructions illustrated with examples from actual trials serve to define relevant statistical approaches, describe their clinical trial applications, and implement the approaches rapidly and efficiently using the power of SAS. Topics reflect the International Conference on Harmonization (ICH) guidelines for the pharmaceutical industry and address important statistical problems encountered in clinical trials. Commonly used methods are covered, including dose-escalation and dose-finding methods that are applied in Phase I and Phase II clinical trials, as well as important trial designs and analysis strategies that are employed in Phase II and Phase III clinical trials, such as multiplicity adjustment, data monitoring, and methods for handling incomplete data. This book also features recommendations from clinical trial experts and a discussion of relevant regulatory guidelines. This new edition includes more examples and case studies, new approaches for addressing statistical problems, and the following new technological updates: SAS procedures used in group sequential trials (PROC SEQDESIGN and PROC SEQTEST) SAS procedures used in repeated measures analysis (PROC GLIMMIX and PROC GEE) macros for implementing a broad range of randomization-based methods in clinical trials, performing complex multiplicity adjustments, and investigating the design and analysis of early phase trials (Phase I dose-escalation trials and Phase II dose-finding trials) Clinical statisticians, research scientists, and graduate students in biostatistics will greatly benefit from the decades of clinical research experience and the ready-to-use SAS macros compiled in this book.
Introduces a range of data analysis problems encountered in drug development and illustrates them using case studies from actual pre-clinical experiments and clinical studies. Includes a discussion of methodological issues, practical advice from subject matter experts, and review of relevant regulatory guidelines.
Pharmaceutical Statistics Using SAS: A Practical Guide offers extensive coverage of cutting-edge biostatistical methodology used in drug development and the practical problems facing today's drug developers. Written by well-known experts in the pharmaceutical industry Alex Dmitrienko, Christy Chuang-Stein, and Ralph D'Agostino, it provides relevant tutorial material and SAS examples to help readers new to a certain area of drug development quickly understand and learn popular data analysis methods and apply them to real-life problems. Step-by-step, the book introduces a wide range of data analysis problems encountered in drug development and illustrates them using a wealth of case studies from actual pre-clinical experiments and clinical studies. The book also provides SAS code for solving the problems. Among the topics addressed are these: drug discovery experiments to identify promising chemical compounds animal studies to assess the toxicological profile of these compounds clinical pha
Introduces a range of data analysis problems encountered in drug development and illustrates them using case studies from actual pre-clinical experiments and clinical studies. Includes a discussion of methodological issues, practical advice from subject matter experts, and review of relevant regulatory guidelines.
Analysis of Clinical Trials Using SAS®: A Practical Guide, Second Edition bridges the gap between modern statistical methodology and real-world clinical trial applications. Tutorial material and step-by-step instructions illustrated with examples from actual trials serve to define relevant statistical approaches, describe their clinical trial applications, and implement the approaches rapidly and efficiently using the power of SAS. Topics reflect the International Conference on Harmonization (ICH) guidelines for the pharmaceutical industry and address important statistical problems encountered in clinical trials. Commonly used methods are covered, including dose-escalation and dose-finding methods that are applied in Phase I and Phase II clinical trials, as well as important trial designs and analysis strategies that are employed in Phase II and Phase III clinical trials, such as multiplicity adjustment, data monitoring, and methods for handling incomplete data. This book also features recommendations from clinical trial experts and a discussion of relevant regulatory guidelines. This new edition includes more examples and case studies, new approaches for addressing statistical problems, and the following new technological updates: SAS procedures used in group sequential trials (PROC SEQDESIGN and PROC SEQTEST) SAS procedures used in repeated measures analysis (PROC GLIMMIX and PROC GEE) macros for implementing a broad range of randomization-based methods in clinical trials, performing complex multiplicity adjustments, and investigating the design and analysis of early phase trials (Phase I dose-escalation trials and Phase II dose-finding trials) Clinical statisticians, research scientists, and graduate students in biostatistics will greatly benefit from the decades of clinical research experience and the ready-to-use SAS macros compiled in this book.
Healthcare is important to everyone, yet large variations in its quality have been well documented both between and within many countries. With demand and expenditure rising, it’s more crucial than ever to know how well the healthcare system and all its components – from staff member to regional network – are performing. This requires data, which inevitably differ in form and quality. It also requires statistical methods, the output of which needs to be presented so that it can be understood by whoever needs it to make decisions. Statistical Methods for Healthcare Performance Monitoring covers measuring quality, types of data, risk adjustment, defining good and bad performance, statistical monitoring, presenting the results to different audiences and evaluating the monitoring system itself. Using examples from around the world, it brings all the issues and perspectives together in a largely non-technical way for clinicians, managers and methodologists. Statistical Methods for Healthcare Performance Monitoring is aimed at statisticians and researchers who need to know how to measure and compare performance, health service regulators, health service managers with responsibilities for monitoring performance, and quality improvement scientists, including those involved in clinical audits.
This thesis presents the results of resonant and non-resonant x-ray scattering experiments demonstrating the control of collective ordering phenomena in epitaxial nickel-oxide and copper-oxide based superlattices. Three outstanding results are reported: (1) LaNiO3-LaAlO3 superlattices with fewer than three consecutive NiO2 layers exhibit a novel spiral spin density wave, whereas superlattices with thicker nickel-oxide layer stacks remain paramagnetic. The magnetic transition is thus determined by the dimensionality of the electron system. The polarization plane of the spin density wave can be tuned by epitaxial strain and spatial confinement of the conduction electrons. (2) Further experiments on the same system revealed an unusual structural phase transition controlled by the overall thickness of the superlattices. The transition between uniform and twin-domain states is confined to the nickelate layers and leaves the aluminate layers unaffected. (3) Superlattices based on the high-temperature superconductor YBa2Cu3O7 exhibit an incommensurate charge density wave order that is stabilized by heterointerfaces. These results suggest that interfaces can serve as a powerful tool to manipulate the interplay between spin order, charge order, and superconductivity in cuprates and other transition metal oxides.
The sudden dissolution of the Soviet Union altered the routines, norms, celebrations, and shared understandings that had shaped the lives of Russians for generations. It also meant an end to the state-sponsored, nonmonetary support that most residents had lived with all their lives. How did Russians make sense of these historic transformations? Serguei Alex. Oushakine offers a compelling look at postsocialist life in Russia. In Barnaul, a major industrial city in southwestern Siberia that has lost 25 percent of its population since 1991, many Russians are finding that what binds them together is loss and despair. The Patriotism of Despair examines the aftermath of the collapse of the Soviet Union, graphically described in spray paint by a graffiti artist in Barnaul: "We have no Motherland." Once socialism disappeared as a way of understanding the world, what replaced it in people's minds? Once socialism stopped orienting politics and economics, how did capitalism insinuate itself into routine practices? Oushakine offers a compelling look at postsocialist life in noncosmopolitan Russia. He introduces readers to the "neocoms": people who mourn the loss of the Soviet economy and the remonetization of transactions that had not involved the exchange of cash during the Soviet era. Moving from economics into military conflict and personal loss, Oushakine also describes the ways in which veterans of the Chechen war and mothers of soldiers who died there have connected their immediate experiences with the country's historical disruptions. The country, the nation, and traumatized individuals, Oushakine finds, are united by their vocabulary of shared pain.
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