Published October, 2014
By Wan Tang and Xin Tu
Publisher: Springer (Springer-Verlag New York Inc.)
Series: Applied Bioinformatics and Biostatistics in Cancer Research
Rating:
Availability: Available

This volume covers classic as well as cutting-edge topics on the analysis of clinical trial data in biomedical and psychosocial research and discusses each topic in an expository and user-friendly fashion.

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Published November, 2013
By David Harrington
Publisher: Springer (Springer-Verlag New York Inc.)
Series: Applied Bioinformatics and Biostatistics in Cancer Research
Rating:
Availability: Available

This book will examine current issues and controversies in the design of clinical trials, including topics in adaptive and sequential designs, the design of correlative genomic studies, the design of studies in which missing data is anticipated.

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Published November, 2012
By Srinivasan Yegnasubramanian and William B. Isaacs
Publisher: Springer (Springer-Verlag New York Inc.)
Series: Applied Bioinformatics and Biostatistics in Cancer Research
Rating:
Availability: Available

This volume provides an overview of modern molecular biological approaches to unbiased discovery in cancer research. It examines advances in molecular biology allowing unbiased analysis of changes in cancer initiation and progression.

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Published September, 2012
By Xin M. Tu, Wan Tang and Xin Tu
Publisher: Springer (Springer-Verlag New York Inc.)
Series: Applied Bioinformatics and Biostatistics in Cancer Research
Rating:
Availability: Available

This volume covers classic as well as cutting-edge topics on the analysis of clinical trial data in biomedical and psychosocial research and discusses each topic in an expository and user-friendly fashion.

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Published February, 2012
By Tuan Pham
Publisher: Springer (Springer-Verlag New York Inc.)
Series: Applied Bioinformatics and Biostatistics in Cancer Research
Rating:
Availability: Available

This volume covers techniques in computational biology and their applications in oncology. It details advanced statistical methods, heuristic algorithms, cluster analysis, data modeling, and image and pattern analysis applied to cancer research.

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Published October, 2011
By David Harrington
Publisher: Springer (Springer-Verlag New York Inc.)
Series: Applied Bioinformatics and Biostatistics in Cancer Research
Rating:
Availability: Available

This book will examine current issues and controversies in the design of clinical trials, including topics in adaptive and sequential designs, the design of correlative genomic studies, the design of studies in which missing data is anticipated.

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Published November, 2010
By Xiaochun Li and Ronghui Xu
Publisher: Springer (Springer-Verlag New York Inc.)
Series: Applied Bioinformatics and Biostatistics in Cancer Research
Rating:
Availability: Available

Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome.

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Published September, 2010
By Srinivasan Yegnasubramanian and William B. Isaacs
Publisher: Springer (Springer-Verlag New York Inc.)
Series: Applied Bioinformatics and Biostatistics in Cancer Research
Rating:
Availability: Available

This volume provides an overview of modern molecular biological approaches to unbiased discovery in cancer research. It examines advances in molecular biology allowing unbiased analysis of changes in cancer initiation and progression.

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Published October, 2009
By Tuan Pham
Publisher: Springer (Springer-Verlag New York Inc.)
Series: Applied Bioinformatics and Biostatistics in Cancer Research
Rating:
Availability: Available

This volume covers techniques in computational biology and their applications in oncology. It details advanced statistical methods, heuristic algorithms, cluster analysis, data modeling, and image and pattern analysis applied to cancer research.

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Published December, 2008
By Xiaochun Li and Ronghui Xu
Publisher: Springer (Springer-Verlag New York Inc.)
Series: Applied Bioinformatics and Biostatistics in Cancer Research
Rating:
Availability: Available

Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome.

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