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Аннотация
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.
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.
Аннотация
Help your organization determine the value of its customer relationships with Segmentation and Lifetime Value Models Using SAS. This book contains a wealth of information that will help you perform analyses to identify your customers and make informed marketing investments. It answers core questions on customer relationship management (CRM), provides an overall framework for thinking about CRM, and offers real-world examples across a variety of industries.
Edward C. Malthouse introduces you to a number of useful models, ranging from simple to more complicated examples, and discusses their applications. You'll learn about segmentation models for identifying groups of customers and about lifetime value models for estimating the future value of the segments. You'll learn how to prepare data and estimate models using Base SAS, SAS/STAT, SAS/IML, and SQL.
Marketing analysts, CRM analysts, database managers, and anyone looking to address the challenges of allocating marketing resources to different customer groups will benefit from the concepts and exercises in this book. Analysts will learn how to approach unique business problems. Managers will gain a sense of what's possible and what to ask of their analytics departments.
This book is part of the SAS Press program.
Edward C. Malthouse introduces you to a number of useful models, ranging from simple to more complicated examples, and discusses their applications. You'll learn about segmentation models for identifying groups of customers and about lifetime value models for estimating the future value of the segments. You'll learn how to prepare data and estimate models using Base SAS, SAS/STAT, SAS/IML, and SQL.
Marketing analysts, CRM analysts, database managers, and anyone looking to address the challenges of allocating marketing resources to different customer groups will benefit from the concepts and exercises in this book. Analysts will learn how to approach unique business problems. Managers will gain a sense of what's possible and what to ask of their analytics departments.
This book is part of the SAS Press program.
Аннотация
JMP Start Statistics: A Guide to Statistics and Data Analysis Using JMP, Fifth Edition, is the perfect mix of software manual and statistics text. Authors John Sall, Ann Lehman, Mia Stephens, and Lee Creighton provide hands-on tutorials with just the right amount of conceptual and motivational material to illustrate how to use the intuitive interface for data analysis in JMP. Each chapter features concept-specific tutorials, examples, brief reviews of concepts, step-by-step illustrations, and exercises.
JMP Start Statistics, Fifth Edition, includes many new features of JMP 10, including an enhanced ability to manage a JMP session by easily tracking open and recently opened JMP tables; scripts, analyses, JMP projects, and other files; vastly expanded tools for instructors to demonstrate statistical concepts and interactive scripts to help students grasp difficult topics; Split-Plot designs with examples; examples of Graph Builder and Control Chart Builder; and new features that make the software easier to use.
This book is part of the SAS Press program.
JMP Start Statistics, Fifth Edition, includes many new features of JMP 10, including an enhanced ability to manage a JMP session by easily tracking open and recently opened JMP tables; scripts, analyses, JMP projects, and other files; vastly expanded tools for instructors to demonstrate statistical concepts and interactive scripts to help students grasp difficult topics; Split-Plot designs with examples; examples of Graph Builder and Control Chart Builder; and new features that make the software easier to use.
This book is part of the SAS Press program.
A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling, Second Edition - Norm O'Rourke, Ph.D., R.Psych.
Аннотация
This easy-to-understand guide makes SEM accessible to all users. This second edition contains new material on sample-size estimation for path analysis and structural equation modeling. In a single user-friendly volume, students and researchers will find all the information they need in order to master SAS basics before moving on to factor analysis, path analysis, and other advanced statistical procedures.
Аннотация
You've just received a new survey of study results, and you need to quickly create custom graphical views of the data. Or, you've completed your analysis, and you need graphs to present the results to your audience, in the style that they prefer. Now, you can create custom graphs quickly and easily with Getting Started with the Graph Template Language in SAS, without having to understand all of the Graph Template Language (GTL) features first.
This book will get you started building graphs immediately and will guide you toward a better understanding of the GTL, one step at a time. It shows you the most common approaches to a variety of graphs along with information that you can use to build more complex graphs from there. Sanjay Matange offers expert tips, examples, and techniques, with a goal of providing you with a solid foundation in using the GTL so that you can progress to more sophisticated, adaptable graphs as you need them.
Ultimately, Getting Started with the Graph Template Language in SAS allows you to bypass the learning curve. It teaches you how to quickly create custom, aesthetically pleasing graphs that present your data with maximum clarity and minimum clutter.
This book is part of the SAS Press program.
This book will get you started building graphs immediately and will guide you toward a better understanding of the GTL, one step at a time. It shows you the most common approaches to a variety of graphs along with information that you can use to build more complex graphs from there. Sanjay Matange offers expert tips, examples, and techniques, with a goal of providing you with a solid foundation in using the GTL so that you can progress to more sophisticated, adaptable graphs as you need them.
Ultimately, Getting Started with the Graph Template Language in SAS allows you to bypass the learning curve. It teaches you how to quickly create custom, aesthetically pleasing graphs that present your data with maximum clarity and minimum clutter.
This book is part of the SAS Press program.
Аннотация
Aimed at econometricians who have completed at least one course in time series modeling, Multiple Time Series Modeling Using the SAS VARMAX Procedure will teach you the time series analytical possibilities that SAS offers today. Estimations of model parameters are now performed in a split second. For this reason, working through the identifications phase to find the correct model is unnecessary. Instead, several competing models can be estimated, and their fit can be compared instantaneously.
Consequently, for time series analysis, most of the Box and Jenkins analysis process for univariate series is now obsolete. The former days of looking at cross-correlations and pre-whitening are over, because distributed lag models are easily fitted by an automatic lag identification method. The same goes for bivariate and even multivariate models, for which PROC VARMAX models are automatically fitted. For these models, other interesting variations arise: Subjects like Granger causality testing, feedback, equilibrium, cointegration, and error correction are easily addressed by PROC VARMAX.
One problem with multivariate modeling is that it includes many parameters, making parameterizations unstable. This instability can be compensated for by application of Bayesian methods, which are also incorporated in PROC VARMAX. Volatility modeling has now become a standard part of time series modeling, because of the popularity of GARCH models. Both univariate and multivariate GARCH models are supported by PROC VARMAX. This feature is especially interesting for financial analytics in which risk is a focus.
This book teaches with examples. Readers who are analyzing a time series for the first time will find PROC VARMAX easy to use; readers who know more advanced theoretical time series models will discover that PROC VARMAX is a useful tool for advanced model building.
Consequently, for time series analysis, most of the Box and Jenkins analysis process for univariate series is now obsolete. The former days of looking at cross-correlations and pre-whitening are over, because distributed lag models are easily fitted by an automatic lag identification method. The same goes for bivariate and even multivariate models, for which PROC VARMAX models are automatically fitted. For these models, other interesting variations arise: Subjects like Granger causality testing, feedback, equilibrium, cointegration, and error correction are easily addressed by PROC VARMAX.
One problem with multivariate modeling is that it includes many parameters, making parameterizations unstable. This instability can be compensated for by application of Bayesian methods, which are also incorporated in PROC VARMAX. Volatility modeling has now become a standard part of time series modeling, because of the popularity of GARCH models. Both univariate and multivariate GARCH models are supported by PROC VARMAX. This feature is especially interesting for financial analytics in which risk is a focus.
This book teaches with examples. Readers who are analyzing a time series for the first time will find PROC VARMAX easy to use; readers who know more advanced theoretical time series models will discover that PROC VARMAX is a useful tool for advanced model building.
Аннотация
Fixed Effects Regression Methods for Longitudinal Data Using SAS, written by Paul Allison, is an invaluable resource for all researchers interested in adding fixed effects regression methods to their tool kit of statistical techniques. First introduced by economists, fixed effects methods are gaining widespread use throughout the social sciences. Designed to eliminate major biases from regression models with multiple observations (usually longitudinal) for each subject (usually a person), fixed effects methods essentially offer control for all stable characteristics of the subjects, even characteristics that are difficult or impossible to measure. This straightforward and thorough text shows you how to estimate fixed effects models with several SAS procedures that are appropriate for different kinds of outcome variables. The theoretical background of each model is explained, and the models are then illustrated with detailed examples using real data. The book contains thorough discussions of the following uses of SAS procedures: PROC GLM for estimating fixed effects linear models for quantitative outcomes, PROC LOGISTIC for estimating fixed effects logistic regression models, PROC PHREG for estimating fixed effects Cox regression models for repeated event data, PROC GENMOD for estimating fixed effects Poisson regression models for count data, and PROC CALIS for estimating fixed effects structural equation models. To gain the most benefit from this book, readers should be familiar with multiple linear regression, have practical experience using multiple regression on real data, and be comfortable interpreting the output from a regression analysis. An understanding of logistic regression and Poisson regression is a plus. Some experience with SAS is helpful, but not required. <p> This book is part of the SAS Press program.
Аннотация
Carpenter's Guide to Innovative SAS Techniques offers advanced SAS programmers an all-in-one programming reference that includes advanced topics not easily found outside the depths of SAS documentation or more advanced training classes. Art Carpenter has written fifteen chapters of advanced tips and techniques, including topics on data summary, data analysis, and data reporting. Special emphasis is placed on DATA step techniques that solve complex data problems. There are numerous examples that illustrate advanced techniques that take advantage of formats, interface with the macro language, and utilize the Output Delivery System. Additional topics include operating system interfaces, table lookup techniques, and the creation of customized reports.
Аннотация
The SAS Programmer's PROC REPORT Handbook: Basic to Advanced Reporting Techniques is intended for programmers of all skill levels. Learn how to link multiple reports, add graphics and logos, and manipulate table of contents values to help refine your programs, macrotize where possible, troubleshoot easily, and get great-looking reports every time. From beginner to advanced, the examples in this book will help you harness all the power and capability of PROC REPORT.
With dozens of useful examples, this book is completely unique in three ways. First, this book describes the default behavior of table of contents nodes and labels, and how to change the nodes inside of PROC REPORT. The chapter also explains how to use PROC DOCUMENT in conjunction with PROC REPORT. Secondly, an entire chapter is dedicated to the troubleshooting of errors, warnings, and notes that are produced by PROC REPORT, including explanations of what generated the log message and how to avoid it. Third, the book explains how to preprocess your data in order to get the best output from PROC REPORT, and it explores reports that require multiple steps to create. Whether you work in banking/finance, pharmaceuticals, the health and life sciences, or government, this handbook is sure to be your new favorite reporting reference.
With dozens of useful examples, this book is completely unique in three ways. First, this book describes the default behavior of table of contents nodes and labels, and how to change the nodes inside of PROC REPORT. The chapter also explains how to use PROC DOCUMENT in conjunction with PROC REPORT. Secondly, an entire chapter is dedicated to the troubleshooting of errors, warnings, and notes that are produced by PROC REPORT, including explanations of what generated the log message and how to avoid it. Third, the book explains how to preprocess your data in order to get the best output from PROC REPORT, and it explores reports that require multiple steps to create. Whether you work in banking/finance, pharmaceuticals, the health and life sciences, or government, this handbook is sure to be your new favorite reporting reference.
Аннотация
This book provides hands-on tutorials with just the right amount of conceptual and motivational material to illustrate how to use the intuitive interface for data analysis in JMP. Each chapter features concept-specific tutorials,
examples, brief reviews of concepts, step-by-step illustrations, and exercises.
Updated for JMP 13, JMP Start Statistics, Sixth Edition includes many new features, including:
The redesigned Formula Editor.
New and improved ways to create formulas in JMP directly from the data table or dialogs.
Interface updates, including improved menu layout.
Updates and enhancements in many analysis platforms.
New ways to get data into JMP and to save and share JMP results.
Many new features that make it easier to use JMP.
examples, brief reviews of concepts, step-by-step illustrations, and exercises.
Updated for JMP 13, JMP Start Statistics, Sixth Edition includes many new features, including:
The redesigned Formula Editor.
New and improved ways to create formulas in JMP directly from the data table or dialogs.
Interface updates, including improved menu layout.
Updates and enhancements in many analysis platforms.
New ways to get data into JMP and to save and share JMP results.
Many new features that make it easier to use JMP.