Bayesian Methods And Computation For Confounding Adjustment In Large Observational Datasets
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| Author | : Douglas E. Faries |
| Publisher | : SAS Press |
| Total Pages | : 0 |
| Release | : 2010 |
| Genre | : Medical care |
| ISBN | : 9781607642275 |
This book guides researchers in performing and presenting high-quality analyses of all kinds of non-randomized studies, including analyses of observational studies, claims database analyses, assessment of registry data, survey data, pharmaco-economic data, and many more applications. The text is sufficiently detailed to provide not only general guidance, but to help the researcher through all of the standard issues that arise in such analyses. Just enough theory is included to allow the reader to understand the pros and cons of alternative approaches and when to use each method. The numerous contributors to this book illustrate, via real-world numerical examples and SAS code, appropriate implementations of alternative methods. The end result is that researchers will learn how to present high-quality and transparent analyses that will lead to fair and objective decisions from observational data. This book is part of the SAS Press program.
| Author | : Christopher H. Schmid |
| Publisher | : CRC Press |
| Total Pages | : 514 |
| Release | : 2020-09-08 |
| Genre | : Mathematics |
| ISBN | : 1351645714 |
Meta-analysis is the application of statistics to combine results from multiple studies and draw appropriate inferences. Its use and importance have exploded over the last 25 years as the need for a robust evidence base has become clear in many scientific areas, including medicine and health, social sciences, education, psychology, ecology, and economics. Recent years have seen an explosion of methods for handling complexities in meta-analysis, including explained and unexplained heterogeneity between studies, publication bias, and sparse data. At the same time, meta-analysis has been extended beyond simple two-group comparisons of continuous and binary outcomes to comparing and ranking the outcomes from multiple groups, to complex observational studies, to assessing heterogeneity of effects, and to survival and multivariate outcomes. Many of these methods are statistically complex and are tailored to specific types of data. Key features Rigorous coverage of the full range of current statistical methodology used in meta-analysis Comprehensive, coherent, and unified overview of the statistical foundations behind meta-analysis Detailed description of the primary methods for both univariate and multivariate data Computer code to reproduce examples in chapters Thorough review of the literature with thousands of references Applications to specific types of biomedical and social science data This book is for a broad audience of graduate students, researchers, and practitioners interested in the theory and application of statistical methods for meta-analysis. It is written at the level of graduate courses in statistics, but will be of interest to and readable for quantitative scientists from a range of disciplines. The book can be used as a graduate level textbook, as a general reference for methods, or as an introduction to specialized topics using state-of-the art methods.
| Author | : Julian P. T. Higgins |
| Publisher | : Wiley |
| Total Pages | : 672 |
| Release | : 2008-11-24 |
| Genre | : Medical |
| ISBN | : 9780470699515 |
Healthcare providers, consumers, researchers and policy makers are inundated with unmanageable amounts of information, including evidence from healthcare research. It has become impossible for all to have the time and resources to find, appraise and interpret this evidence and incorporate it into healthcare decisions. Cochrane Reviews respond to this challenge by identifying, appraising and synthesizing research-based evidence and presenting it in a standardized format, published in The Cochrane Library (www.thecochranelibrary.com). The Cochrane Handbook for Systematic Reviews of Interventions contains methodological guidance for the preparation and maintenance of Cochrane intervention reviews. Written in a clear and accessible format, it is the essential manual for all those preparing, maintaining and reading Cochrane reviews. Many of the principles and methods described here are appropriate for systematic reviews applied to other types of research and to systematic reviews of interventions undertaken by others. It is hoped therefore that this book will be invaluable to all those who want to understand the role of systematic reviews, critically appraise published reviews or perform reviews themselves.
| Author | : Agency for Health Care Research and Quality (U.S.) |
| Publisher | : Government Printing Office |
| Total Pages | : 236 |
| Release | : 2013-02-21 |
| Genre | : Medical |
| ISBN | : 1587634236 |
This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)
| Author | : Richard McElreath |
| Publisher | : CRC Press |
| Total Pages | : 488 |
| Release | : 2018-01-03 |
| Genre | : Mathematics |
| ISBN | : 1315362619 |
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.
| Author | : Timothy L. Lash |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 200 |
| Release | : 2011-04-14 |
| Genre | : Medical |
| ISBN | : 0387879595 |
Bias analysis quantifies the influence of systematic error on an epidemiology study’s estimate of association. The fundamental methods of bias analysis in epi- miology have been well described for decades, yet are seldom applied in published presentations of epidemiologic research. More recent advances in bias analysis, such as probabilistic bias analysis, appear even more rarely. We suspect that there are both supply-side and demand-side explanations for the scarcity of bias analysis. On the demand side, journal reviewers and editors seldom request that authors address systematic error aside from listing them as limitations of their particular study. This listing is often accompanied by explanations for why the limitations should not pose much concern. On the supply side, methods for bias analysis receive little attention in most epidemiology curriculums, are often scattered throughout textbooks or absent from them altogether, and cannot be implemented easily using standard statistical computing software. Our objective in this text is to reduce these supply-side barriers, with the hope that demand for quantitative bias analysis will follow.
| Author | : Mark J. van der Laan |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 628 |
| Release | : 2011-06-17 |
| Genre | : Mathematics |
| ISBN | : 1441997822 |
The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.
| Author | : Judea Pearl |
| Publisher | : John Wiley & Sons |
| Total Pages | : 162 |
| Release | : 2016-01-25 |
| Genre | : Mathematics |
| ISBN | : 1119186862 |
CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.
| Author | : Andrew Stevens |
| Publisher | : SAGE |
| Total Pages | : 540 |
| Release | : 2001-01-02 |
| Genre | : Medical |
| ISBN | : 1847876757 |
′This handbook is an excellent reflection of the growing maturity and methodological sophistication of the field of Health Technology Assessment. The Handbook covers a spectrum of issues, from primary evidence (clinical trials) through reviews and meta-analysis, to identifying and filling gaps in the evidence. Up-to-date, clearly written, and well-edited, the handbook is a needed addition to any personal or professional library dealing with Health Technology Assessment.′ Professor David Banta, TNO Prevention and Health, The Netherlands ′This text presents the most advanced knowledge on methodology in health care research, and will form the backbone of many future studies′ - Paula Roberts, Nurse Researcher The `effectiveness revolution′ both in research and clinical practice, has tested available methods for health services research to the extreme. How far can observational methods, routine data and qualitative methods be used in health care evaluation? What cost and outcome measures are appropriate, and how should data be gathered? With the support of over two million pounds from the British Health Technology Assessment Research Programme, the research project for this Handbook has led to both a synthesis of all of the existing knowledge in these areas and an agenda for future debate and research. The chapters and their authors have been selected through a careful process of peer review and provide a coherent and complete approach to the field. The handbook has been a unique collaboration between internationally regarded clinicians, statisticians, epidemiologists, social scientists, health economists and ethicists. It provides the most advanced thinking and the most authoritative resource for a state of the art review of methods of evaluating health care and will be required reading for anyone involved in health services research and management.
| Author | : Paul Gustafson |
| Publisher | : CRC Press |
| Total Pages | : 213 |
| Release | : 2003-09-25 |
| Genre | : Mathematics |
| ISBN | : 0203502760 |
Mismeasurement of explanatory variables is a common hazard when using statistical modeling techniques, and particularly so in fields such as biostatistics and epidemiology where perceived risk factors cannot always be measured accurately. With this perspective and a focus on both continuous and categorical variables, Measurement Error and Misclassi
