Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Statistical Diagnostics for Cancer PDF full book. Access full book title Statistical Diagnostics for Cancer by Matthias Dehmer. Download full books in PDF and EPUB format.
Author: Matthias Dehmer Publisher: John Wiley & Sons ISBN: 3527665455 Category : Medical Languages : en Pages : 301
Book Description
This ready reference discusses different methods for statistically analyzing and validating data created with high-throughput methods. As opposed to other titles, this book focusses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or less complex biological network. From a methodological point of view, the well balanced contributions describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. Furthermore, since the availability of sufficient computer power in recent years has shifted attention from parametric to nonparametric methods, the methods presented here make use of such computer-intensive approaches as Bootstrap, Markov Chain Monte Carlo or general resampling methods. Finally, due to the large amount of information available in public databases, a chapter on Bayesian methods is included, which also provides a systematic means to integrate this information. A welcome guide for mathematicians and the medical and basic research communities.
Author: Matthias Dehmer Publisher: John Wiley & Sons ISBN: 3527665455 Category : Medical Languages : en Pages : 301
Book Description
This ready reference discusses different methods for statistically analyzing and validating data created with high-throughput methods. As opposed to other titles, this book focusses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or less complex biological network. From a methodological point of view, the well balanced contributions describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. Furthermore, since the availability of sufficient computer power in recent years has shifted attention from parametric to nonparametric methods, the methods presented here make use of such computer-intensive approaches as Bootstrap, Markov Chain Monte Carlo or general resampling methods. Finally, due to the large amount of information available in public databases, a chapter on Bayesian methods is included, which also provides a systematic means to integrate this information. A welcome guide for mathematicians and the medical and basic research communities.
Author: Craig Beam Publisher: Springer Science & Business Media ISBN: 1475735715 Category : Medical Languages : en Pages : 242
Book Description
Biostatistics is defined as much by its application as it is by theory. This book provides an introduction to biostatistical applications in modern cancer research that is both accessible and valuable to the cancer biostatistician or to the cancer researcher, learning biostatistics. The topical areas include active areas of the application of biostatistics to modern cancer research: survival analysis, screening, diagnostics, spatial analysis and the analysis of microarray data. Biostatistics is an essential component of basic and clinical cancer research. The text, authored by distinguished figures in the field, addresses clinical issues in statistical analysis. The spectrum of topics discussed ranges from fundamental methodology to clinical and translational applications.
Author: Margaret Sullivan Pepe Publisher: ISBN: 0198509847 Category : Biochemical markers Languages : en Pages : 319
Book Description
This book describes statistical concepts and techniques for evaluating medical diagnostic tests and biomarkers for detecting disease. More generally, the techniques pertain to the statistical classification problem for predicting a dichotomous outcome. Measures for quantifying test accuracy are described including sensitivity, specificity, predictive values, diagnostic likelihood ratios and the Receiver Operating Characteristic Curve that is commonly used for continuous and ordinal valued tests. Statistical procedures are presented for estimating and comparing them. Regression frameworks for assessing factors that influence test accuracy and for comparing tests while adjusting for such factors are presented. This book presents many worked examples of real data and should be of interest to practicing statisticians or quantitative researchers involved in the development of tests for classification or prediction in medicine.
Author: Danny A. Milner, Jr., MD Publisher: Springer Publishing Company ISBN: 161705268X Category : Medical Languages : en Pages : 207
Book Description
This essential guide provides a clear, accessible review of the use of statistics in pathology studies. Spanning topics such as exploratory data analysis and descriptive statistics as well as the use of comparative statistics, concordance analysis, categorical and continuous data regression analyses, count data, survival analyses, decision point and clustering analysis, and more, this practical book comprehensively demystifies all the statistical knowledge paramount to working in the field. Throughout the guide, the author team highlights common errors and pitfalls that occur when performing tests and when interpreting data and make suggestions for how to avoid mistakes. Chapters are uniformly structured for ease of use and each chapter concludes with reviewquestions to facilitate self-assessment of the reader's skill in use of statistical methods. Statistics for Pathologists teaches trainees, junior investigators, and seasoned pathologists how to look at their data from the point of view of a statistician and determine what tests should be done, how the data and test should be presented, and how to use the tests practically. Learning statistical applications can greatly enhance and simplify the skills necessary to review and present data accurately and this basic understanding of statistics is critical in pathology-related work. Key Features Clear, concise overviews of every relevant statistical test with application in pathology-related research Includes real published studies to provide examples of use of the tests and interpretation of data Emphasizes how to avoid common errors and pitfalls when conducting tests and interpreting data Provides self-assessment review questions and answers in each chapter Comes with downloadable datasets for the reader so that they can perform statistical analysis tied to the book with popular stats programs
Author: Heide Schatten Publisher: Springer ISBN: 3319956930 Category : Science Languages : en Pages : 135
Book Description
This volume covers classic and modern cell and molecular biology of prostate cancer, as well as novel biomarkers, inflammation, centrosome pathologies, microRNAs, cancer initiation novel biomarkers, inflammation, centrosome pathologies, microRNAs, cancer initiation and genetics, epigenetics, mitochondrial dysfunctions and apoptosis, cancer stem cells, angiogenesis and progression to metastasis, and treatment strategies including clinical trials related to prostate cancer. Cell & Molecular Biology of Prostate Cancer is one of two companion books comprehensively addressing the biology and clinical aspects of prostate cancer. Prostate Cancer: Molecular & Diagnostic Imaging and Treatment Stategies, the companion volume, discusses both classic and the most recent imaging approaches including analysis of needle biopsies, applications of nanoparticle probes and peptide-based radiopharmaceuticals for detection, early diagnosis and treatment of prostate cancer. Taken together, these volumes form one comprehensive and invaluable contribution to the literature.
Author: Francesco Sardanelli Publisher: Springer Science & Business Media ISBN: 8847011337 Category : Medical Languages : en Pages : 244
Book Description
The aim of this book is to present statistical problems and methods in a friendly way to radiologists, emphasizing statistical issues and methods most frequently used in radiological studies (e.g., nonparametric tests, analysis of intra- and interobserver reproducibility, comparison of sensitivity and specificity among different imaging modality, difference between clinical and screening application of diagnostic tests, ect.). The tests will be presented starting from a radiological "problem" and all examples of statistical methods applications will be "radiological".
Author: Shuichi Shinmura Publisher: Springer ISBN: 9811359989 Category : Medical Languages : en Pages : 419
Book Description
This book shows how to decompose high-dimensional microarrays into small subspaces (Small Matryoshkas, SMs), statistically analyze them, and perform cancer gene diagnosis. The information is useful for genetic experts, anyone who analyzes genetic data, and students to use as practical textbooks. Discriminant analysis is the best approach for microarray consisting of normal and cancer classes. Microarrays are linearly separable data (LSD, Fact 3). However, because most linear discriminant function (LDF) cannot discriminate LSD theoretically and error rates are high, no one had discovered Fact 3 until now. Hard-margin SVM (H-SVM) and Revised IP-OLDF (RIP) can find Fact3 easily. LSD has the Matryoshka structure and is easily decomposed into many SMs (Fact 4). Because all SMs are small samples and LSD, statistical methods analyze SMs easily. However, useful results cannot be obtained. On the other hand, H-SVM and RIP can discriminate two classes in SM entirely. RatioSV is the ratio of SV distance and discriminant range. The maximum RatioSVs of six microarrays is over 11.67%. This fact shows that SV separates two classes by window width (11.67%). Such easy discrimination has been unresolved since 1970. The reason is revealed by facts presented here, so this book can be read and enjoyed like a mystery novel. Many studies point out that it is difficult to separate signal and noise in a high-dimensional gene space. However, the definition of the signal is not clear. Convincing evidence is presented that LSD is a signal. Statistical analysis of the genes contained in the SM cannot provide useful information, but it shows that the discriminant score (DS) discriminated by RIP or H-SVM is easily LSD. For example, the Alon microarray has 2,000 genes which can be divided into 66 SMs. If 66 DSs are used as variables, the result is a 66-dimensional data. These signal data can be analyzed to find malignancy indicators by principal component analysis and cluster analysis.
Author: Xin Sun Publisher: ISBN: Category : Languages : en Pages :
Book Description
This report illustrates how to use two statistic methods to investigate the performance of a new technique to detect breast cancer and lung cancer at early stages. The two methods include logistic regression and classification and regression tree (CART). It is found that the technique is effective in detecting breast cancer and lung cancer, with both sensitivity and specificity close to 0.9. But the ability of this technique to predict the actual stages of cancer is low. The age variable improves the ability of logistic regression in predicting the existence of breast cancer for the samples used in this report. But since the sample sizes are small, it is impossible to conclude that including the age variable helps the prediction of breast cancer. Including the age variable does not improve the ability to predict the existence of lung cancer. If the age variable is excluded, CART and logistic regression give a very close result.
Author: Trevor F. Cox Publisher: CRC Press ISBN: 1000601102 Category : Mathematics Languages : en Pages : 334
Book Description
Cancer is a dreaded disease. One in two people will be diagnosed with cancer within their lifetime. Medical Statistics for Cancer Studies shows how cancer data can be analysed in a variety of ways, covering cancer clinical trial data, epidemiological data, biological data, and genetic data. It gives some background in cancer biology and genetics, followed by detailed overviews of survival analysis, clinical trials, regression analysis, epidemiology, meta-analysis, biomarkers, and cancer informatics. It includes lots of examples using real data from the author’s many years of experience working in a cancer clinical trials unit. Features: A broad and accessible overview of statistical methods in cancer research Necessary background in cancer biology and genetics Details of statistical methodology with minimal algebra Many examples using real data from cancer clinical trials Appendix giving statistics revision.
Author: Toshiro Tango Publisher: Springer Science & Business Media ISBN: 1441915729 Category : Mathematics Languages : en Pages : 240
Book Description
This book is intended to provide a text on statistical methods for detecting clus ters and/or clustering of health events that is of interest to ?nal year undergraduate and graduate level statistics, biostatistics, epidemiology, and geography students but will also be of relevance to public health practitioners, statisticians, biostatisticians, epidemiologists, medical geographers, human geographers, environmental scien tists, and ecologists. Prerequisites are introductory biostatistics and epidemiology courses. With increasing public health concerns about environmental risks, the need for sophisticated methods for analyzing spatial health events is immediate. Further more, the research area of statistical tests for disease clustering now attracts a wide audience due to the perceived need to implement wide ranging monitoring systems to detect possible health related bioterrorism activity. With this background and the development of the geographical information system (GIS), the analysis of disease clustering of health events has seen considerable development over the last decade. Therefore, several excellent books on spatial epidemiology and statistics have re cently been published. However, it seems to me that there is no other book solely focusing on statistical methods for disease clustering. I hope that readers will ?nd this book useful and interesting as an introduction to the subject.
Author: Matthias Dehmer
Author: Matthias Dehmer
Author: Craig Beam
Author: Margaret Sullivan Pepe
Author: Danny A. Milner, Jr., MD
Author: Heide Schatten
Author: Francesco Sardanelli
Author: Shuichi Shinmura
Author: Xin Sun
Author: Trevor F. Cox
Author: Toshiro Tango