Bayes and Empirical Bayes Methods for Data Analysis, Second Edition Review

Average Reviews:

(More customer reviews)This book features a deep and focused lesson on Bayes and Empirical Bayes Methods. It goes through the key topics as conjugate priors, MCMC methods (non iteratives and iteratives as the well known Gibbs samplining and metropolitis hastings algorithms), model selection methods (as bayes factor) and issues related as model robusteness.
The Approach is increasingly formal and deeply complex, allowing for getting the basics or diving into more complex knowledge according to your former background. You need at least a good understanding of Frequentist statistic to be able to follow the reasonings. Each chapter allow you to stop at some point without losing the thread. Last part of the book is in fact deep knowledge demanding.
The most interesting point of this book according to my very limited statistics background is that it makes good comparations with the frequentist approach (classical approaches as confidence intervals and point estimators), checking performance of either method. Even, it features some combination of both approaches getting some bayessian intervals.
As a negative point, I would say that examples are hard to follow for someone with limited bakground and too much complex. They really do not clear me up enough.
All in all, is a very profitable book for jumping into bayesian methods.

Click Here to see more reviews about: Bayes and Empirical Bayes Methods for Data Analysis, Second Edition

In recent years, Bayes and empirical Bayes (EB) methods have continued to increase in popularity and impact. Building on the first edition of their popular text, Carlin and Louis introduce these methods, demonstrate their usefulness in challenging applied settings, and show how they can be implemented using modern Markov chain Monte Carlo (MCMC) methods. Their presentation is accessible to those new to Bayes and empirical Bayes methods, while providing in-depth coverage valuable to seasoned practitioners.With its broad appeal as a text for those in biomedical science, education, social science, agriculture, and engineering, this second edition offers a relatively gentle and comprehensive introduction for students and practitioners already familiar with more traditional frequentist statistical methods. Focusing on practical tools for data analysis, the book shows how properly structured Bayes and EB procedures typically have good frequentist and Bayesian performance, both in theory and in practice.

Click here for more information about Bayes and Empirical Bayes Methods for Data Analysis, Second Edition

Read More...

Bayesian Methods for Measures of Agreement (Chapman & Hall/CRC Biostatistics Series) Review

Average Reviews:

(More customer reviews)This is the scond book of Lyle Broemeling that I am reviewing for Amazon. I met him at the Joint Statistical Meetings a few years ago when he was just retiring from M.D. Anderson. in recent years M. D. Anderson has become a leader in designing Bayesian adaptive designs of clinical trials. This is mainly due to the leadership of Don Berry who came to head up the biostatistics group at M. D. Anderson several years ago when he was attracted away from Duke. Broemeling benefitted from the arrival of Berry because he was establishe there as a Bayesian and had written a book on Bayesian analysis many years earlier.
Now that he is retired from M. D. Anderson he is writing applied biostatistics texts applying Bayesian methods to specialized topics. The first one which I reviewed earlier on amazon was on diagnostic testing and this one is to analyze measures of agreement among judges. The two books are both scholarly written and authoritative and clear. They both also provide many real examples based on Lyle's vast experience at M. D. Anderson.
A few years ago I was supporting the company BioImaging in the development of their protocols for medical imaging data from patients in oncology clinical trials. I learned that an important aspect of determining the efficacy of a drug against a particular cancer tumor. This performance is usually measured by individual ranking from radiologist who read the scans over time and assess growth or shrinkage of the tumor after being treated by a drug. Typically there are two or three readers and the rating of progression or remission depends on a concensus of the radiologists assessments.
This is exactly the problem Broemeling faced at at M. D. Anderson and he has a wealth of applications in the setting of oncology trials. Broemeling details the history of the develop of methods used to reach a conclusion. He provides a wealth of examples and also includes interesting examples from sports including an analysis of a famous boxing match between Lennox Lewis and Evander Holyfield. He deals methodically with the case of two raters (where an adjudicator general resolve the conflicting cases) and then three or more raters where things get more complicated.
Modern Bayesian approaches are demonstrated using the winBugs software. Broemeling provides the code in the winBugs language to handle various examples. This approach involves Markov Chain Monte Carlo methods. Examples are explained in detail and illustrated very carefully.
Broemeling also provides a history of the various statistics used to measure agrrement between readers or judges. Another example that struck me as very interesting is a forgery case where a signature was forged to produce a fake will. Usually in forgery cases the methods are used to find differences in the signature that are large enough to assert that they came from different people. However in this example the forged signature was traced from the original persons sample signature. So in the case the objective was to show that the cases are too similar not to have been forged. We are able to do this because we can show repeated signatures from the same hand will have more variability than the traced signature. So in this case the hired statisticians showed that the two signatures are much too similar for the second one to be real and independent of each other.
Bayesian sample size estimation is also covered in the text. It is a great reference book for anyone who does oncology trials and appreciates the advantages of the Bayesian approach. The Kappa measure is the one that is given the most attention in the book.

Click Here to see more reviews about: Bayesian Methods for Measures of Agreement (Chapman & Hall/CRC Biostatistics Series)

Using WinBUGS to implement Bayesian inferences of estimation and testing hypotheses, Bayesian Methods for Measures of Agreement presents useful methods for the design and analysis of agreement studies. It focuses on agreement among the various players in the diagnostic process.The author employs a Bayesian approach to provide statistical inferences based on various models of intra- and interrater agreement. He presents many examples that illustrate the Bayesian mode of reasoning and explains elements of a Bayesian application, including prior information, experimental information, the likelihood function, posterior distribution, and predictive distribution. The appendices provide the necessary theoretical foundation to understand Bayesian methods as well as introduce the fundamentals of programming and executing the WinBUGS software.Taking a Bayesian approach to inference, this hands-on book explores numerous measures of agreement, including the Kappa coefficient, the G coefficient, and intraclass correlation. With examples throughout and end-of-chapter exercises, it discusses how to successfully design and analyze an agreement study.

Get 14% OFF

Click here for more information about Bayesian Methods for Measures of Agreement (Chapman & Hall/CRC Biostatistics Series)

Read More...

Mathematical Epidemiology (Lecture Notes in Mathematics / Mathematical Biosciences Subseries) Review

Average Reviews:

(More customer reviews)well done an excellent tool as a reference and introduction to epidemiological modeling. clear and simple. a tool for intermediate and ADVANCE epidemioogia

Click Here to see more reviews about: Mathematical Epidemiology (Lecture Notes in Mathematics / Mathematical Biosciences Subseries)

Based on lecture notes of two summer schools with a mixed audience from mathematical sciences, epidemiology and public health, this volume offers a comprehensive introduction to basic ideas and techniques in modeling infectious diseases, for the comparison of strategies to plan for an anticipated epidemic or pandemic, and to deal with a disease outbreak in real time. It covers detailed case studies for diseases including pandemic influenza, West Nile virus, and childhood diseases. Models for other diseases including Severe Acute Respiratory Syndrome, fox rabies, and sexually transmitted infections are included as applications. Its chapters are coherent and complementary independent units. In order to accustom students to look at the current literature and to experience different perspectives, no attempt has been made to achieve united writing style or unified notation.Notes on some mathematical background (calculus, matrix algebra, differential equations, and probability) have been prepared and may be downloaded at the web site of the Centre for Disease Modeling (www.cdm.yorku.ca).

Get 21% OFF

Click here for more information about Mathematical Epidemiology (Lecture Notes in Mathematics / Mathematical Biosciences Subseries)

Read More...

Modelling and Quantitative Methods in Fisheries, Second Edition Review

Average Reviews:

(More customer reviews)In my opinion, Malcolm Haddon has managed a "tour de force" with this book. He not only covered most of the modern methods of quantitative analysis and modelling in fisheries science but he did so in a clear and relatively simple language. His book is approachable to all biologists with a basic understanding of mathematics and statistics. Yet, he managed to cover both the theoretical underpinnings of the methods and the practical aspects of their use (options, pitfalls ... etc.). In addition, the book gives MS Excel examples of the methods which should allow those of us who are not programmers to fully appreciate the methods by using them interactively. The Excel spreadsheets are also available for download on two web sites.

Click Here to see more reviews about: Modelling and Quantitative Methods in Fisheries, Second Edition

With numerous real-world examples, Modelling and Quantitative Methods in Fisheries, Second Edition provides an introduction to the analytical methods used by fisheries' scientists and ecologists. By following the examples using Excel, readers see the nuts and bolts of how the methods work and better understand the underlying principles. Excel workbooks are available for download from CRC Press Online.In this second edition, the author has revised all chapters and improved a number of the examples. This edition also includes two entirely new chapters:Characterization of Uncertainty covers asymptotic errors and likelihood profiles and develops a generalized Gibbs sampler to run a Markov chain Monte Carlo analysis that can be used to generate Bayesian posteriorsSized-Based Models implements a fully functional size-based stock assessment model using abalone as an exampleThis book continues to cover a broad range of topics related to quantitative methods and modelling. It offers a solid foundation in the skills required for the quantitative study of marine populations. Explaining important and relatively complex ideas and methods in a clear manner, the author presents full, step-by-step derivations of equations as much as possible to enable a thorough understanding of the models and methods.

Get 9% OFF

Click here for more information about Modelling and Quantitative Methods in Fisheries, Second Edition

Read More...

The Frailty Model (Statistics for Biology and Health) Review

Average Reviews:

(More customer reviews)The authors are academics who have done serious research in survival analysis and are very familiar with frailty models. The topic comes up when time to event data for one event is correlated with the time to event data for another or other events. This topic is sometimes referred to a subject in multivariate survival analysis or the analysis of clustered survival data.
As a professional biostatistician with a keen interest in survival models I have attended professional meetings in recent years and heard the term Frailty Model mentioned but I didn't know what it was. There of course is the natural connotation of weakness as in a feeble or frail person. But the actual formal dtatisticial meaning was a mystery. Other books that I am very familar with deal in part with frailty models but this is to my knowledge the first serious text dedicated to this topic. It also covers related methods to accomplish the same goal such as copulas (another term common in recent books and literature but one I was not familiar with either). For example Philip Hougaard wrote the first advanced text on multivariate survival models and covers parametric forms of frailty models. Klein and Moeschberger wrote a generaal survival analysis book that includes a chapter on semi-parameric fraility models. It showa how the EM algorithm is used to estimate parameters of the models. Ibrahim and colleague wrote a book on Bayesian methods in survival analysis and cover the Bayesian approach to both semi-parametric and parametric fraility models. Therneau and Grambsch wrote a recent book on the Cox proportional hazard model and its extensions. It included information on semi-parametric frailty models using the penalized partial likelihood approach to estimation.
This book is a well-written introduction to fraility models that includes all these methods provides real world examples and good explanations on how to interpret the results. The examples are illustrated using the freeware language R. This book could serve as either an undergraduate or graduate text in statistical methods and is a great reference for biostatisticians.

Click Here to see more reviews about: The Frailty Model (Statistics for Biology and Health)

Readers will find in the pages of this book a treatment of the statistical analysis of clustered survival data. Such data are encountered in many scientific disciplines including human and veterinary medicine, biology, epidemiology, public health and demography. A typical example is the time to death in cancer patients, with patients clustered in hospitals. Frailty models provide a powerful tool to analyze clustered survival data. In this book different methods based on the frailty model are described and it is demonstrated how they can be used to analyze clustered survival data. All programs used for these examples are available on the Springer website.

Get 20% OFF

Click here for more information about The Frailty Model (Statistics for Biology and Health)

Read More...

Bayesian Adaptive Methods for Clinical Trials (Chapman & Hall/CRC Biostatistics Series) Review

Average Reviews:

(More customer reviews)In the pharmaceutical industry adaptive designs are currently the rage because of their many potential advantages due to their flexibility. It allows you to stop early for efficacy or futility. It can do drug dose selection more easily and may have patients on inferior treatment for smaller amounts of time. There have already been four or five books published from the frequentist point of view. This is the first serious text on adaptive designs using the Bayesian approach. Pharmaceutical companies including Johnson and Johnson, Eli Lilly, Pfizer, Merck, Novartis, Novo Nordisk, Millennium, AMAG and GlaxoSmithKline have all been successful at running adaptive trials. Merck for example has already completed more than 40 adaptive design trials. Such trials can be done in phase II, phase III or a combining of phases II and III in a single adaptive trial. Merck claims to have completed over 40 adaptive trials. The M D Anderson Medical Center at UT Houston runs hundreds of adaptive trials (all as far as I know using the Bayesian methodology). Don Berry runs the biostatistics group at M D Anderson and he and his son scott own a consulting group that helps companies run Bayesian adaptive designs. Eli Lilly has been one of their clients on a drug trial and Biosense Webster, a J& J company, used them for a Bayesian trial on one of their ablation catheters. Scott Berry isone of the authors of this book and a lot of the book is devoted to work of Berry first at Duke and then later at M D Anderson and Berry Consultants.
Adaptive designs have logistic problems but companies have been able to overcome the problems motivated by the overall time and money saving benefits. All types of studies are illustrated from phase I through phase III and the examples are real and practical. Even when taking the Bayesian approach issues of frequentist properties for the designs comes up. Missing data, multiple testing, type I error and power of the test conditional and unconidtional are important when the frequentist approach is applied. The authors admit that both frequentist and Bayesian properties for a design are important and can be evaluated through simulation.
Although adaptive designs can be implemented effectively using either the Bayesian or the frequentist approaches. But Bayesian trials are a little more natural and simpler. This is the right book to get if you are interested in Bayesian methods.

Click Here to see more reviews about: Bayesian Adaptive Methods for Clinical Trials (Chapman & Hall/CRC Biostatistics Series)

Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer's disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adaptive Methods for Clinical Trials explores the growing role of Bayesian thinking in the rapidly changing world of clinical trial analysis.The book first summarizes the current state of clinical trial design and analysis and introduces the main ideas and potential benefits of a Bayesian alternative. It then gives an overview of basic Bayesian methodological and computational tools needed for Bayesian clinical trials. With a focus on Bayesian designs that achieve good power and Type I error, the next chapters present Bayesian tools useful in early (Phase I) and middle (Phase II) clinical trials as well as two recent Bayesian adaptive Phase II studies: the BATTLE and ISPY-2 trials. In the following chapter on late (Phase III) studies, the authors emphasize modern adaptive methods and seamless Phase II-III trials for maximizing information usage and minimizing trial duration. They also describe a case study of a recently approved medical device to treat atrial fibrillation. The concluding chapter covers key special topics, such as the proper use of historical data, equivalence studies, and subgroup analysis.For readers involved in clinical trials research, this book significantly updates and expands their statistical toolkits. The authors provide many detailed examples drawing on real data sets. The R and WinBUGS codes used throughout are available on supporting websites.

Get 18% OFF

Click here for more information about Bayesian Adaptive Methods for Clinical Trials (Chapman & Hall/CRC Biostatistics Series)

Read More...

Modeling in Medical Decision Making: A Bayesian Approach (Statistics in Practice) Review

Average Reviews:

(More customer reviews)This is a nice readable book about decision theory from the Bayesian perspective. In addition to providing the foundations of Bayesian decision theory including utility theory the author prvides many real world research examples including meta-analyses.
Unlike his more recent and highly statistically sophisticated text on Bayesian decision theory this concentrates on medical examples and his written at a level to appeal to clinicians as well as statisticians. Decision trees and methods toevaluate quality of life years are introduced and used in the examples.

Click Here to see more reviews about: Modeling in Medical Decision Making: A Bayesian Approach (Statistics in Practice)

Click here for more information about Modeling in Medical Decision Making: A Bayesian Approach (Statistics in Practice)

Read More...

Nonparametric Regression Methods for Longitudinal Data Analysis: Mixed-Effects Modeling Approaches (Wiley Series in Probability and Statistics) Review

Average Reviews:

(More customer reviews)Very good introductory text for nonparametric regression. This book first gives motivation for the processes, outlines the methods of smoothing, and then covers various regression techniques. Not particularly mathematical, although some linear algebra knowledge is necessary. Very accessible as a first course. Does not go very in-depth into the theory of any of the methods, however.

Click Here to see more reviews about: Nonparametric Regression Methods for Longitudinal Data Analysis: Mixed-Effects Modeling Approaches (Wiley Series in Probability and Statistics)

Incorporates mixed-effects modeling techniques for more powerful and efficient methodsThis book presents current and effective nonparametric regression techniques for longitudinal data analysis and systematically investigates the incorporation of mixed-effects modeling techniques into various nonparametric regression models. The authors emphasize modeling ideas and inference methodologies, although some theoretical results for the justification of the proposed methods are presented.With its logical structure and organization, beginning with basic principles, the text develops the foundation needed to master advanced principles and applications. Following a brief overview, data examples from biomedical research studies are presented and point to the need for nonparametric regression analysis approaches. Next, the authors review mixed-effects models and nonparametric regression models, which are the two key building blocks of the proposed modeling techniques.The core section of the book consists of four chapters dedicated to the major nonparametric regression methods: local polynomial, regression spline, smoothing spline, and penalized spline. The next two chapters extend these modeling techniques to semiparametric and time varying coefficient models for longitudinal data analysis. The final chapter examines discrete longitudinal data modeling and analysis.Each chapter concludes with a summary that highlights key points and also provides bibliographic notes that point to additional sources for further study. Examples of data analysis from biomedical research are used to illustrate the methodologies contained throughout the book. Technical proofs are presented in separate appendices.With its focus on solving problems, this is an excellent textbook for upper-level undergraduate and graduate courses in longitudinal data analysis. It is also recommended as a reference for biostatisticians and other theoretical and applied research statisticians with an interest in longitudinal data analysis. Not only do readers gain an understanding of the principles of various nonparametric regression methods, but they also gain a practical understanding of how to use the methods to tackle real-world problems.

Get 17% OFF

Click here for more information about Nonparametric Regression Methods for Longitudinal Data Analysis: Mixed-Effects Modeling Approaches (Wiley Series in Probability and Statistics)

Read More...

Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) Review

Average Reviews:

(More customer reviews)Issues of missing data in longitudinal studies are very important in the design and analysis of clinical trials. This is such an important statistical topic, that many excellent books have been written about it. One of the earliest and a landmark text was the book by Rubin and Little which was recently updated in the second edition. Mixed linear models for longitudinal data provide an effective method for dealing with several types of missingness as does multiple imputation. Pattern mixture models are also very useful. Molenberghs and Kennard, Verbeke and Molenberghs and Rubin all cover these topic well in their excellent texts.
What then is the advantage of this text by Daniels and Hogan?
1. It is slightly more current than the others
2. It combines theory and application very nicely
3. A series of seven real data sets from real clinical trials and epidemiologic studies are presented up front in Chapter 1 and used throughout to illustrate practical advantages and disadvantages of the various techniques covered in the latter chapters
4. It covers Bayesian modeling and sensitivity analysis in more depth that most of its competitors
Only Molenberghs and Kennard match it in the depth of coverage on theory and applications. But they do not provide the coverage of Bayesian methods the way Daniels and Hogan do.
For these reasons I recommend this book to the practicing biostatisticians working on clinical trials even if the texts listed below are alresdy on their bookshelves.
I) Diggle, P. J., Heagerty, P., Liang, K.-Y. and Zeger, S. L. (2002). "Analysis of Longitudinal Data" 2nd Edition. Oxfrod University Press, Oxford.
II) Fitzmaurice, G. M., Laird, N.M. and Ware, J. H. (2004). "Applied ongitudinal Analysis". John Wiley & Sons, New York.
III) Little, R. J. A. and Rubin, D. B. (2002) "Statistical Analysis with Missing Data" 2nd Edition, John Wiley & Sons, New York
IV) Molenberghs, G and Kennard, M. G. (2007). "Missing Data in Clinical Studies" John Wiley & Sons, Chichester.
V) Molenberghs, G. and Verbeke, G. (2005). "Models for Discrete Longitudinal Data". Springer-Verlag, New York.
VI) Pinheiro, J. C. and Bates, D. M. (2000). "Mixed Effects Models in S and S-Plus". Springer-Verlag, New York.
VII) Rubin, D. B. (1987). "Multiple Imputation for Nonresponse in Surveys" John Wiley & Sons, New York.
VIII) Tsiatis, A. A. (2006). "Semiparametric Theory and Missing Data" Dpringer-Verlag, New York.
IX) Verbeke, G and Molenberghs, G. (1997). "Linear Mixed Models in Practice: A SAS-Oriented Approach" Springer-Verlag, New York.
X) Verbeke, G and Molenberghs, G. (2000). "Linear Mixed Models for Longitudinal Data" Springer-Verlag, New York.


Click Here to see more reviews about: Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

Drawing from the authors' own work and from the most recent developments in the field, Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis describes a comprehensive Bayesian approach for drawing inference from incomplete data in longitudinal studies. To illustrate these methods, the authors employ several data sets throughout that cover a range of study designs, variable types, and missing data issues.
The book first reviews modern approaches to formulate and interpret regression models for longitudinal data. It then discusses key ideas in Bayesian inference, including specifying prior distributions, computing posterior distribution, and assessing model fit. The book carefully describes the assumptions needed to make inferences about a full-data distribution from incompletely observed data. For settings with ignorable dropout, it emphasizes the importance of covariance models for inference about the mean while for nonignorable dropout, the book studies a variety of models in detail. It concludes with three case studies that highlight important features of the Bayesian approach for handling nonignorable missingness.
With suggestions for further reading at the end of most chapters as well as many applications to the health sciences, this resource offers a unified Bayesian approach to handle missing data in longitudinal studies.

Get 14% OFF

Click here for more information about Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

Read More...

Hierarchical Modeling and Analysis for Spatial Data (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) Review

Average Reviews:

(More customer reviews)I've bought several spatial statistics books over the years and found they generally fall into one of two categories; oversimplified or cover-to-cover matrix notation, neither of which is very useful for my research. However, this book is "just right," bridging these two extremes. It briefly covers the basics of both point and areal analysis, then gives the reader the tools for more advanced (i.e., realistic) analysis. They devote a chapter to Bayesian basics, which is needed for the last 4 or 5 chapters. The last few chapters weave together a detailed discussion on a variety of hierarchical models and current published results. Most importantly this book offers quite a bit of the necessary R and Winbugs code. Although many of their examples are from the public health world, the techniques and code are easily adapted to natural resource data - my personal focus.

Click Here to see more reviews about: Hierarchical Modeling and Analysis for Spatial Data (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis, or written at a level often inaccessible to those lacking a strong background in mathematical statistics.Hierarchical Modeling and Analysis for Spatial Data is the first accessible, self-contained treatment of hierarchical methods, modeling, and data analysis for spatial and spatio-temporal data. Starting with overviews of the types of spatial data, the data analysis tools appropriate for each, and a brief review of the Bayesian approach to statistics, the authors discuss hierarchical modeling for univariate spatial response data, including Bayesian kriging and lattice (areal data) modeling. They then consider the problem of spatially misaligned data, methods for handling multivariate spatial responses, spatio-temporal models, and spatial survival models. The final chapter explores a variety of special topics, including spatially varying coefficient models.This book provides clear explanations, plentiful illustrations --some in full color--a variety of homework problems, and tutorials and worked examples using some of the field's most popular software packages.. Written by a team of leaders in the field, it will undoubtedly remain the primary textbook and reference on the subject for years to come.

Get 23% OFF

Click here for more information about Hierarchical Modeling and Analysis for Spatial Data (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

Read More...

Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data (Cambridge Medicine) Review

Average Reviews:

(More customer reviews)I used this book as the text for a biostatistics class that used STATA as the statistitical package. I found the organization, problems, and the STATA output the book provides, all very helpful. In addition, as I moved systematically through the book, the tips regarding using the STATA features were key to my learning many of the practical aspects of the STATA program.

Click Here to see more reviews about: Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data (Cambridge Medicine)

For biomedical researchers, the new edition of this standard text guides readers in the selection and use of advanced statistical methods and the presentation of results to clinical colleagues. It assumes no knowledge of mathematics beyond high school level and is accessible to anyone with an introductory background in statistics. The Stata statistical software package is used to perform the analyses, in this edition employing the intuitive version 10. Topics covered include linear, logistic and Poisson regression, survival analysis, fixed-effects analysis of variance, and repeated-measure analysis of variance. Restricted cubic splines are used to model non-linear relationships. Each method is introduced in its simplest form and then extended to cover more complex situations. An appendix will help the reader select the most appropriate statistical methods for their data. The text makes extensive use of real data sets available online through Vanderbilt University.

Get 17% OFF

Click here for more information about Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data (Cambridge Medicine)

Read More...

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

Average Reviews:

(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.


Click Here to see more reviews about: Modeling Survival Data: Extending the Cox Model (Statistics for Biology and Health)

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.

Get 20% OFF

Click here for more information about Modeling Survival Data: Extending the Cox Model (Statistics for Biology and Health)

Read More...