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Frontiers of biostatistical methods ...

Crowley, John.

Frontiers of biostatistical methods and applications in clinical oncology

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Frontiers of biostatistical methods and applications in clinical oncologyedited by Shigeyuki Matsui, John Crowley.
其他作者:	Matsui, Shigeyuki.
出版者:	Singapore :Springer Singapore :2017.
面頁冊數:	xiii, 438 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Biometry.
電子資源:	http://dx.doi.org/10.1007/978-981-10-0126-0
ISBN:	9789811001260$q(electronic bk.)

Frontiers of biostatistical methods and applications in clinical oncology
Frontiers of biostatistical methods and applications in clinical oncology[electronic resource] /edited by Shigeyuki Matsui, John Crowley. - Singapore :Springer Singapore :2017. - xiii, 438 p. :ill., digital ;24 cm.

1. Phase I clinical trials -- 2. Phase II clinical trials -- 3. Phase III clinical trials -- 4. Evaluation of surrogate endpoints -- 5. Disease Screening/development of diagnosis -- 6. Analysis of omics data (genomic signature developments etc) -- 7. Causal inference in clinical trials and observational studies.

This book presents the state of the art of biostatistical methods and their applications in clinical oncology. Many methodologies established today in biostatistics have been brought about through its applications to the design and analysis of oncology clinical studies. This field of oncology, now in the midst of evolution owing to rapid advances in biotechnologies and cancer genomics, is becoming one of the most promising disease fields in the shift toward personalized medicine. Modern developments of diagnosis and therapeutics of cancer have also been continuously fueled by recent progress in establishing the infrastructure for conducting more complex, large-scale clinical trials and observational studies. The field of cancer clinical studies therefore will continue to provide many new statistical challenges that warrant further progress in the methodology and practice of biostatistics. This book provides a systematic coverage of various stages of cancer clinical studies. Topics from modern cancer clinical trials include phase I clinical trials for combination therapies, exploratory phase II trials with multiple endpoints/treatments, and confirmative biomarker-based phase III trials with interim monitoring and adaptation. It also covers important areas of cancer screening, prognostic analysis, and the analysis of large-scale molecular data in the era of big data.

ISBN: 9789811001260$q(electronic bk.)

Standard No.: 10.1007/978-981-10-0126-0doiSubjects--Topical Terms:

182039
Biometry.

LC Class. No.: QH323.5 / .F76 2017

Dewey Class. No.: 570.15195

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520 $a This book presents the state of the art of biostatistical methods and their applications in clinical oncology. Many methodologies established today in biostatistics have been brought about through its applications to the design and analysis of oncology clinical studies. This field of oncology, now in the midst of evolution owing to rapid advances in biotechnologies and cancer genomics, is becoming one of the most promising disease fields in the shift toward personalized medicine. Modern developments of diagnosis and therapeutics of cancer have also been continuously fueled by recent progress in establishing the infrastructure for conducting more complex, large-scale clinical trials and observational studies. The field of cancer clinical studies therefore will continue to provide many new statistical challenges that warrant further progress in the methodology and practice of biostatistics. This book provides a systematic coverage of various stages of cancer clinical studies. Topics from modern cancer clinical trials include phase I clinical trials for combination therapies, exploratory phase II trials with multiple endpoints/treatments, and confirmative biomarker-based phase III trials with interim monitoring and adaptation. It also covers important areas of cancer screening, prognostic analysis, and the analysis of large-scale molecular data in the era of big data.
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