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Refining the Concept of Scientific Inference When Working with Big Data
The concept of utilizing big data to enable scientific discovery has generated tremendous excitement and investment from both private and public sectors over the past decade, and expectations continue to grow. Using big data analytics to identify complex patterns hidden inside volumes of data that have never been combined could accelerate the rate of scientific discovery and lead to the development of beneficial technologies and products. However, producing actionable scientific knowledge from such large, complex data sets requires statistical models that produce reliable inferences (NRC, 2013). Without careful consideration of the suitability of both available data and the statistical model...
Ecological Inference
Drawing upon the recent explosion of research in the field, a diverse group of scholars surveys the latest strategies for solving ecological inference problems, the process of trying to infer individual behavior from aggregate data. The uncertainties and information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, marketing research by business, and policy analysis by governments. This wide-ranging collection of essays offers many fresh and important contributions to the study of ecological inference.
The Joy of Leadership
Think of the last time you were at your best and working in the zone. Now compare that to all the time you’ve spent just going through the motions. How would you quantify the difference between the two in terms of how you felt and what you produced? Would you rate it as a 5% difference. . . a 50% difference? Most people would describe the gulf between those two states of being as vast—as deep and wide as the Grand Canyon. For quality of experience, engagement, productivity, and just the sheer joy of living, the majority of people asked to rate the difference between flourishing and floundering found it to be around a factor of 10. In their work with managers and executives at leading com...
Modern Epidemiology
Now in a fully revised Fourth Edition, Modern Epidemiology remains the gold standard text in this complex and evolving field. This edition continues to provide comprehensive coverage of the principles and methods for the design, analysis, and interpretation of epidemiologic research. Featuring a new format allowing space for margin notes, this edition • Reflects both the conceptual development of this evolving science and the increasing role that epidemiology plays in improving public health and medicine. • Features new coverage of methods such as agent-based modeling, quasi-experimental designs, mediation analysis, and causal modeling. • Updates coverage of methods such as concepts of...
Demystifying Causal Inference
This book provides an accessible introduction to causal inference and data analysis with R, specifically for a public policy audience. It aims to demystify these topics by presenting them through practical policy examples from a range of disciplines. It provides a hands-on approach to working with data in R using the popular tidyverse package. High quality R packages for specific causal inference techniques like ggdag, Matching, rdrobust, dosearch etc. are used in the book. The book is in two parts. The first part begins with a detailed narrative about John Snow’s heroic investigations into the cause of cholera. The chapters that follow cover basic elements of R, regression, and an introdu...
Redesigning the Clinical Effectiveness Research Paradigm
Recent scientific and technological advances have accelerated our understanding of the causes of disease development and progression, and resulted in innovative treatments and therapies. Ongoing work to elucidate the effects of individual genetic variation on patient outcomes suggests the rapid pace of discovery in the biomedical sciences will only accelerate. However, these advances belie an important and increasing shortfall between the expansion in therapy and treatment options and knowledge about how these interventions might be applied appropriately to individual patients. The impressive gains made in Americans' health over the past decades provide only a preview of what might be possib...
Solving the Brain Puzzle
Receiving a diagnosis of multiple sclerosis (MS), Alzheimer’s disease, Parkinson’s disease, or some other brain-related illness is devastating. It feels like life, as you know it, is over, and you are powerless to do anything about it. Your future may seem like nothing but a long black tunnel of decreasing cognitive function, declining mobility, depression, and premature death. Even your physician may share this gloomy view. The good news is, you have more control over your brain health than you think! With the exception of cancer, many brain illnesses can be reversed through a combination of diet, exercise, supplements, proper sleep, avoiding and removing toxins from the body, and takin...
Strength in Numbers: The Rising of Academic Statistics Departments in the U. S.
Statistical science as organized in formal academic departments is relatively new. With a few exceptions, most Statistics and Biostatistics departments have been created within the past 60 years. This book consists of a set of memoirs, one for each department in the U.S. created by the mid-1960s. The memoirs describe key aspects of the department’s history -- its founding, its growth, key people in its development, success stories (such as major research accomplishments) and the occasional failure story, PhD graduates who have had a significant impact, its impact on statistical education, and a summary of where the department stands today and its vision for the future. Read here all about how departments such as at Berkeley, Chicago, Harvard, and Stanford started and how they got to where they are today. The book should also be of interests to scholars in the field of disciplinary history.
Handbook of Matching and Weighting Adjustments for Causal Inference
An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures.
Statistical Methods in Epilepsy
Epilepsy research promises new treatments and insights into brain function, but statistics and machine learning are paramount for extracting meaning from data and enabling discovery. Statistical Methods in Epilepsy provides a comprehensive introduction to statistical methods used in epilepsy research. Written in a clear, accessible style by leading authorities, this textbook demystifies introductory and advanced statistical methods, providing a practical roadmap that will be invaluable for learners and experts alike. Topics include a primer on version control and coding, pre-processing of imaging and electrophysiological data, hypothesis testing, generalized linear models, survival analysis,...
