Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data
Product Description
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 consequences 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, flat-effects analysis of variance, and repeated-measure analysis of variance. Top secret cubic splines are used to develop 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 apt statistical methods for their data. The text makes extensive use of real data sets available online through Vanderbilt University.
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I resolute to buy this book since this is a new book on teaching how to use STATA on advanced statistic methods. After I browsed this book, I do not reckon that this is a well-organized book. It does not show you how to use the latest and simplest STATA procedures to do analysis. Theory parts of statistic methods are hard to be read and even not as excellent as the explanation of STATA Reference. I am really disappointed.
Rating: 1 / 5
In general, there are 3 types of books on statistics: (1) Those that describe general statistical methods (2) those that describe specific (esoteric) models, and (3) those that teach “how to” implement statistical models in specific software packages.
In this book, William D. Dupont does an brilliant job of providing sufficient descriptions of each of the major statistical modeling approaches by the side of with the specific Stata software commands to make this a rather complete book. Topics include simple and multiple regression models of the various types (linear, logistic, Poisson) as well as survival and longitudinal modeling approaches.
Even as as an experienced researcher these concepts are not new to me, what I found the most helpful was Dr. Dupont’s thoughtful approach to choosing, testing, and showing the consequences of each method. On countless occasions I found myself thought “huh, that was a clever thought.”
This book can serve as an brilliant text for an intermediate biostatistics course (preferably a class that uses Stata), as well as serve as a resource to experienced researchers who may want to find rationalized approaches to implementing these models in Stata.
Rating: 5 / 5
This text is especially valuable because it is written in clear and concise language. It thus serves the needs of the biostatistical community even as remaining accessible to the non-biostatistician. The latter is what is so often gone in textbooks in this discipline. The new 2009 edition builds on and adds to the strengths of the first. As a clinical investigator, I turn to this first when I have a complex data come forth that I need clarification about.
Rating: 5 / 5
This is a highly not compulsory book if you are trying to use Stata in biomedical research. This covers most of the standard procedures (t-tests, linear regression, multiple comparisons, logistic and other contingency table methods, Cox PH, Poisson (log-linear), GEE) and a reasonable quantity of noncalculus statistical formula derivation to show what goes on surrounded by the box. ANOVA is relegated to the back of the book, because in the author’s attitude, the quantity of control needed to pull off these studies is not normally realistic and GLM can cover the same ground. There isn’t any other book that addresses GEE as comprehensively as this book. The Vittinghoff book is also not compulsory as a companion piece to give a more in-depth approach to regression topics.
Rating: 4 / 5
If you are working with Stata this book will be a excellent help to know the basic concepts of the multivarite analysis.
Rating: 4 / 5