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Sexualizing Cancer
"Starting in 2005, people in the US and Europe were inundated with media coverage announcing the between cervical cancer and the sexually transmitted virus HPV. Within a year, product ads promoted a vaccine targeting cancer's viral cause and girls and women were enrolled as early consumers of this new cancer vaccine. The knowledge of HPV's links to other cancers, notably anal and oral, soon followed, which identified new at-risk populations and ignited a variety of gendered and sexual issues related to cancer prevention. Sexualizing Cancer is the first book dedicated to the emergence and proliferation of the HPV vaccine. It shows how the late twentieth century scientific breakthrough that id...
Vitamins and Minerals
This accessible reference profiles the vitamins and minerals most important to human health, presenting information in an easy-to-use format and summarizing the findings of key research studies. Everyone knows that vitamins and minerals are nonnegotiable components of optimal health. But what exactly do these substances do in the body, and how much of each is needed? What happens if an individual ingests too little or too much of a particular vitamin or mineral? Which foods are the best sources of them, and are dietary supplements a safe alternative? Do certain vitamins and minerals offer protection against certain diseases and medical conditions? Vitamins and Minerals: Fact versus Fiction p...
Medical Sexism
Doctors routinely deny patients access to hormonal birth control prescription refills, and this issue has broad interest for feminism, biomedical ethics, and applied ethics in general. Medical Sexism argues that such practices violate a variety of legal and moral standards, including medical malpractice, informed consent, and human rights. Jill B. Delston makes the case that medical sexism serves as a major underlying cause of these systemic and persistent violations. Delston also considers other common abuses in the medical field, such as policy on abortion access and treatment in childbirth. Delston argues that sexism is a better explanation for the widespread abuse of patient autonomy in reproductive health and health care generally. Identifying, addressing, and rooting out medical sexism is necessary to successfully protect medical and moral values.
Multivariate Kernel Smoothing and Its Applications
Kernel smoothing has greatly evolved since its inception to become an essential methodology in the data science tool kit for the 21st century. Its widespread adoption is due to its fundamental role for multivariate exploratory data analysis, as well as the crucial role it plays in composite solutions to complex data challenges. Multivariate Kernel Smoothing and Its Applications offers a comprehensive overview of both aspects. It begins with a thorough exposition of the approaches to achieve the two basic goals of estimating probability density functions and their derivatives. The focus then turns to the applications of these approaches to more complex data analysis goals, many with a geometr...
Absolute Risk
Absolute Risk: Methods and Applications in Clinical Management and Public Health provides theory and examples to demonstrate the importance of absolute risk in counseling patients, devising public health strategies, and clinical management. The book provides sufficient technical detail to allow statisticians, epidemiologists, and clinicians to build, test, and apply models of absolute risk. Features: Provides theoretical basis for modeling absolute risk, including competing risks and cause-specific and cumulative incidence regression Discusses various sampling designs for estimating absolute risk and criteria to evaluate models Provides details on statistical inference for the various sampli...
Sufficient Dimension Reduction
Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern recognition, and medicine, because they are fields that produce large datasets with a large number of variables. Sufficient Dimension Reduction: Methods and Applications with R introduces the basic theories and the main methodologies, provides practical and easy-to-use algorithms and computer codes to implement these methodologies, and surveys the recent advances at the frontiers of this field. Features Provides comprehensive coverage of this emerging research field. Synthesizes a wide variety of ...
Medical Risk Prediction Models
Medical Risk Prediction Models: With Ties to Machine Learning is a hands-on book for clinicians, epidemiologists, and professional statisticians who need to make or evaluate a statistical prediction model based on data. The subject of the book is the patient’s individualized probability of a medical event within a given time horizon. Gerds and Kattan describe the mathematical details of making and evaluating a statistical prediction model in a highly pedagogical manner while avoiding mathematical notation. Read this book when you are in doubt about whether a Cox regression model predicts better than a random survival forest. Features: All you need to know to correctly make an online risk c...
Nonparametric Models for Longitudinal Data
Nonparametric Models for Longitudinal Data with Implementations in R presents a comprehensive summary of major advances in nonparametric models and smoothing methods with longitudinal data. It covers methods, theories, and applications that are particularly useful for biomedical studies in the era of big data and precision medicine. It also provides flexible tools to describe the temporal trends, covariate effects and correlation structures of repeated measurements in longitudinal data. This book is intended for graduate students in statistics, data scientists and statisticians in biomedical sciences and public health. As experts in this area, the authors present extensive materials that are...
Probabilistic Foundations of Statistical Network Analysis
Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, all of which play a central role in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay between basic principles of statistical inference, empirical properties of network data, and technical concepts from probabili...
