Computational Methods In Biomedical Research (chapman & Hall/crc Biostatistics Series)
by Ravindra Khattree /
2007 / English / PDF
7.2 MB Download
Continuing advances in biomedical research and statistical methods
call for a constant stream of updated, cohesive accounts of new
developments so that the methodologies can be properly implemented
in the biomedical field. Responding to this need,
Continuing advances in biomedical research and statistical methods
call for a constant stream of updated, cohesive accounts of new
developments so that the methodologies can be properly implemented
in the biomedical field. Responding to this need,Computational
Methods in Biomedical Research
Computational
Methods in Biomedical Research explores important current and
emerging computational statistical methods that are used in
biomedical research.
explores important current and
emerging computational statistical methods that are used in
biomedical research.
Written by active researchers in the field, this authoritative
collection covers a wide range of topics. It introduces each
topic at a basic level, before moving on to more advanced
discussions of applications. The book begins with microarray data
analysis, machine learning techniques, and mass
spectrometry-based protein profiling. It then uses state space
models to predict US cancer mortality rates and provides an
overview of the application of multistate models in analyzing
multiple failure times. The book also describes various Bayesian
techniques, the sequential monitoring of randomization tests,
mixed-effects models, and the classification rules for repeated
measures data. The volume concludes with estimation methods for
analyzing longitudinal data.
Written by active researchers in the field, this authoritative
collection covers a wide range of topics. It introduces each
topic at a basic level, before moving on to more advanced
discussions of applications. The book begins with microarray data
analysis, machine learning techniques, and mass
spectrometry-based protein profiling. It then uses state space
models to predict US cancer mortality rates and provides an
overview of the application of multistate models in analyzing
multiple failure times. The book also describes various Bayesian
techniques, the sequential monitoring of randomization tests,
mixed-effects models, and the classification rules for repeated
measures data. The volume concludes with estimation methods for
analyzing longitudinal data.
Supplying the knowledge necessary to perform sophisticated
statistical analyses, this reference is a must-have for anyone
involved in advanced biomedical and pharmaceutical research. It
will help in the quest to identify potential new drugs for the
treatment of a variety of diseases.
Supplying the knowledge necessary to perform sophisticated
statistical analyses, this reference is a must-have for anyone
involved in advanced biomedical and pharmaceutical research. It
will help in the quest to identify potential new drugs for the
treatment of a variety of diseases.
