Many texts are excellent sources of knowledge about individual
statistical tools (general linear models, survival analysis, missing
data, etc.), but the art of data analysis is about choosing and using
multiple tools. Instead of presenting isolated techniques, this text
emphasizes problem solving strategies that address the many issues
arising when developing multivariable models using real data and not
standard textbook examples. It includes imputation methods for
dealing with missing data effectively, methods for dealing with
nonlinear relationships and for making the estimation of
transformations a formal part of the modeling process, methods for
dealing with ``too many variables to analyze and not enough
observations,'' and powerful model validation techniques based on the
bootstrap. This text realistically deals with model uncertainty, and
its effects on inference, to achieve ``safe data mining.'' It also
presents many graphical methods for communicating complex regression
models to non-statisticians.
Regression Modeling Strategies presents full scale case
studies of non-trivial datasets instead of over-simplified
illustrations of each method. These case studies use freely available
high-level
S-Plus and R functions that make the multiple
imputation, model building, validation, and interpretation tasks
described in the book relatively easy to do. Most of the methods in
this text apply to all regression models, but special emphasis is
given to some of the most popular ones: the linear multiple regression
model based on ordinary least squares, the binary logistic model, two
logistic models for ordinal responses, parametric survival regression
models, and the Cox semi-parametric survival model.
This text is intended for Masters' or PhD level graduate students who
have had a general introductory probability and statistics course and
who are well versed in ordinary multiple regression and algebra. The
book is also intended to serve as a reference for data analysts and
statistical methodologists, as it contains an up-to-date survey and
bibliography of modern statistical modeling techniques.
The author, Frank E Harrell Jr, is Professor of Biostatistics and
Statistics and head of the Division of Biostatistics and Epidemiology,
Department of Health Evaluation Sciences, University of Virginia
School of Medicine, Charlottesville, USA. He received his Ph.D. in
biostatistics from the University of North Carolina, Chapel Hill. Dr.
Harrell has been involved in statistical computing and modeling since
1969. He has published numerous predictive models and articles on
applied statistics, medical research, and clinical trials. He is on
the editorial board of
Statistics in Medicine, is an editorial
consultant for the
Journal of Clinical Epidemiology, is
co-managing editor of the journal
Health Services and Outcomes
Research Methodology, and is a consultant to the US Food and Drug
Administration and to the pharmaceutical industry. He teaches
graduate level courses in biostatistical
modeling, statistical computing and graphics, and advanced data
analysis.
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JohnBock - 27 Oct 2011