Overview

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.

Full Product Details

Author:   Ravindra Khattree ,  Dayanand Naik
Publisher:   Taylor & Francis Ltd
Imprint:   Chapman & Hall/CRC
Weight:   0.453kg
ISBN:  

9780367388010

ISBN 10:   0367388014
Pages:   432
Publication Date:   18 October 2019
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Preface. Microarray Data Analysis. Machine Learning Techniques for Bioinformatics: Fundamentals and Applications. Machine Learning Methods for Cancer Diagnosis and Prognostication. Protein Profiling for Disease Proteomics with Mass Spectrometry: Computational Challenges. Predicting US Cancer Mortality Counts Using State Space Models. Analyzing Multiple Failure Time Data Using SAS® Software. Mixed-Effects Models for Longitudinal Virologic and Immunologic HIV Data. Bayesian Computational Methods in Biomedical Research. Sequential Monitoring of Randomization Tests. Proportional Hazards Mixed-Effects Models and Applications. Classification Rules for Repeated Measures Data from Biomedical Research. Estimation Methods for Analyzing Longitudinal Data Occurring in Biomedical Research. Index.

Reviews

Author Information

Ravindra Khattree, Dayanand N. Naik

Tab Content 6

Author Website:  

Customer Reviews

Recent Reviews

No review item found!

Add your own review!

Countries Available

All regions