Advanced Log-Linear Models Using SAS Review

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(More customer reviews)Another Zelterman classic. It's amazing what he can do in SAS.

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Daniel Zelterman applies his extensive SAS knowledge and biostatistics experience to illustrate how to use the GENMOD procedure to analyze log-linear models for categorical data. His wide variety of examples illustrate the statistical applications PROC GENMOD can perform. He thoroughly describes the models, provides real data examples, supplies the necessary code, and explains the output from GENMOD. The topics covered include: the Pearson goodness of fit statistic; tables of categorical data; a review of log-linear model methods for rectangular tables of categorical data; extrapolation methods to estimate population size; new models and distributions for statistical analysis of data; and issues in power analysis and estimating sample size in experiments. The models take advantage of the wide class of generalized linear models and use real data from pharmaceutical studies and epidemiology, wildlife, and government statistics. Statisticians who have a basic under!standing both of SAS and the analysis of categorical data will greatly benefit from this book. The discussion of each model and method emphasizes statistical aspects, such as interpretation of results, rather than programming skills. The numerous examples are used to motivate the theory and methods as they are discussed.

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Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications Review

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(More customer reviews)Excellent book with detailed illustration of steps using Eminer. Great for users of Eminer with understanding of data mining methods.

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Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications demonstrates how to make the fullest use of SAS Enterprise Miner software. Kattamuri Sarma provides an in-depth explanation of the methodology and the theory behind each tool that he covers, and then shows you how the software performs the tasks. Step by step, you'll be able to compare manual calculations with the calculations that are performed by SAS Enterprise Miner. Examples from the insurance and banking industries are based on simulated, but realistic, data. The approaches discussed in this book are relevant to any industry.
Here are a few of the topics discussed in detail:
data collection and data cleaning
data exploration
decision trees and regression trees
logistic regression models
neural networks
variable selection and variable transformation

You need this book if you are a graduate student interested in predictive modeling, an expert in data mining who is not familiar with SAS Enterprise Miner, or a business analyst who needs an introduction to predictive modeling using SAS Enterprise Miner. To get the most from this book, you should be familiar with elements of statistical inference and probability, simple algebra, ordinary least squares, logistic regression, and Base SAS software.

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Modeling Survival Data: Extending the Cox Model (Statistics for Biology and Health) Review

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(More customer reviews)Terry Therneau is a research statistician at the Mayo Clinic and Patricia Grambsch is a Professor of Biostatistics at the University of Minnesota. The Cox proportional hazards model has been one of the key methods for analyzing survival data with covariates for the last 25 years. Proportionality is a key assumption that limits its use. There has long been a need to find methods which diagnose when the hazard rates are not proportional and provide alternative methods in such situations. Using the theory of counting processes the authors are able to extend the Cox model to more general situations including multiple/correlated event data using either marginal models or random effects (frailty) models. Time dependent covariates are also covered. Some of the theory of martigales and counting processes is included to make the book self-contained. Generalized residuals are used to identify outlying and influential observations (analogous to ordinary regression) and also to assess the proportional hazards assumption.
Although the topics are advanced and the mathematical level is high the book is designed for practitioners, emphasizing applications and providing numerous examples, many from the authors' experience. Statistical analyses are done in SAS and SPlus. The authors tend to use SAS for data management and analysis and SPlus for diagnostics and other plots. Therneau is an expert programmer who has written much of the necessary software in both systems.
Therneau gave an excellent short course that I attended a couple of years ago at the Joint Statistical Meetings based on a draft of the text. The finished product is as good as I expected.
The appendices include SAS and S-Plus tutorials on survival analysis and provide SAS Macros and S functions to apply the new methodology.
The book is now (December 2008) in its 6th printing which is another testament to its value and popularity and a nice deal at amazon's current price of $87. But O'Quigley's book is out now too. So maybe Terry and Patricia should be thinking about doing a revision if they don't already have one in the works.


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This book is for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyze multiple/correlated event data using marginal and random effects. The focus is on actual data examples, the analysis and interpretation of results, and computation. The book shows how these new methods can be implemented in SAS and S-Plus, including computer code, worked examples, and data sets.

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Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling Review

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(More customer reviews)Snijders and Bosker's Multilevel Analysis is mathematically demanding but more readable than Raudenbush and Bryk's Hierarchical Linear Models. Snijders and Bosker's text would be much more useful for the less-experienced reader if it contained a directory identifying essential material. As is often the case with books of this kind, it is difficult to distinguish between really important topics, those crucial to understanding multilevel models, and ancillary topics which can be treated as non-essential, at least for the first pass through this dense and difficult material.
It would also help if the authors located multilevel modeling within a statistical context likely to contain material that readers already understand. As it is, multilevel analysis is treated as a separate and new topic, and is readily accessible only to those with generalized mathematical maturity.
The authors do, however, cover a broad range of pertinent material. Thus, while it is not a good choice for beginners looking for a self-instructional tool, the statistically sophisticated reader will find it to be an excellent reference. While just as difficult to understand as the rest of the book, Snijders and Bosker's develop some really informative and interesting examples of three-level models.
This text has been in print for a decade, and is still widely used. I've found that as I become more familiar with multilevel modeling, the book becomes more valuable.

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The Second Edition of this classic text introduces the main methods, techniques, and issues involved in carrying out multilevel modeling and analysis. Snijders and Boskers' book is an applied, authoritative, and accessible introduction to the topic, providing readers with a clear conceptual and practical understanding of all the main issues involved in designing multilevel studies and conducting multilevel analysis. This book has been comprehensively revised and updated since the last edition, and now includes guides to modeling using HLM, MLwiN, SAS, Stata including GLLAMM, R, SPSS, Mplus, WinBugs, Latent Gold, and Mix.

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