Computational Methods in Biomedical Research: 1st Edition (Hardback) book cover

Computational Methods in Biomedical Research

1st Edition

Edited by Ravindra Khattree, Dayanand Naik

Chapman and Hall/CRC

432 pages | 5 Color Illus. | 43 B/W Illus.

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Hardback: 9781584885771
pub: 2007-12-12
eBook (VitalSource) : 9780429139574
pub: 2007-12-12
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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.


"This edited volume covers a broad array of topics of modern relevance in biomedical research. … this book should be in every library that supports biostatistical and biomedical research…"

Biometrics, March 2009

Table of Contents


Microarray Data Analysis

Susmita Datta, Somnath Datta, Rudolph S. Parrish, and Caryn M. Thompson

Machine Learning Techniques for Bioinformatics: Fundamentals and Applications

Jarosław Meller and Michael Wagner

Machine Learning Methods for Cancer Diagnosis and Prognostication

Anne-Michelle Noone and Mousumi Banerjee

Protein Profiling for Disease Proteomics with Mass Spectrometry: Computational Challenges

Dayanand N. Naik and Michael Wagner

Predicting US Cancer Mortality Counts Using State Space Models

Kaushik Ghosh, Ram C. Tiwari, Eric J. Feuer, Kathleen A. Cronin, and Ahmedin Jemal

Analyzing Multiple Failure Time Data Using SAS® Software

Joseph C. Gardiner, Lin Liu, and Zhehui Luo

Mixed-Effects Models for Longitudinal Virologic and Immunologic HIV Data

Florin Vaida, Pulak Ghosh, and Lin Liu

Bayesian Computational Methods in Biomedical Research

Hedibert F. Lopes, Peter Müller, and Nalini Ravishanker

Sequential Monitoring of Randomization Tests

Yanqiong Zhang and William F. Rosenberger

Proportional Hazards Mixed-Effects Models and Applications

Ronghui Xu and Michael Donohue

Classification Rules for Repeated Measures Data from Biomedical Research

Anuradha Roy and Ravindra Khattree

Estimation Methods for Analyzing Longitudinal Data Occurring in Biomedical Research

N. Rao Chaganty and Deepak Mav


About the Series

Chapman & Hall/CRC Biostatistics Series

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General
MEDICAL / Pharmacology
SCIENCE / Biotechnology