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Computational Methods in Biomedical Research

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advanced computational statistics in medicine
AML High Risk
Bayesian inference methods
bioinformatics applications
biomedical data analysis
cancer mortality prediction
Category=PS
Category=UY
CS Correlation Structure
Data Set
Em Step
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
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eq_science
forest
frailty
Frailty Models
hazards
HIV RNA
HIV-1 RNA
Information Fraction
Kth Inspection
Linear Rank Test
Log10 HIV-1 RNA
Logic Regression
longitudinal study techniques
machine
MC
mixed-effects statistical modeling
model
models
Permuted Block Design
Platelet Recovery
Pop1 Pop2 Pop1 Pop2 Pop1
proportional
Protein Protein Interaction Interfaces
random
Random Allocation Rule
Random Forest
Random Intercept Logistic Model
Score Function
Sequential Monitoring
Shared Frailty Model
support
Urn Design
Van Der Waerden Scores
vector

Product details

  • ISBN 9781584885771
  • Weight: 746g
  • Dimensions: 156 x 234mm
  • Publication Date: 12 Dec 2007
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Description

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 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.

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.
About
Ravindra Khattree, Dayanand N. Naik