Propensity Score Analysis
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Propensity Score Analysis
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Author : Shenyang Guo
language : en
Publisher: SAGE
Release Date : 2015
Propensity Score Analysis written by Shenyang Guo and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Mathematics categories.
Provides readers with a systematic review of the origins, history, and statistical foundations of Propensity Score Analysis (PSA) and illustrates how it can be used for solving evaluation and causal-inference problems.
Practical Propensity Score Methods Using R
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Author : Walter Leite
language : en
Publisher: SAGE Publications
Release Date : 2016-10-28
Practical Propensity Score Methods Using R written by Walter Leite and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-28 with Social Science categories.
Practical Propensity Score Methods Using R by Walter Leite is a practical book that uses a step-by-step analysis of realistic examples to help students understand the theory and code for implementing propensity score analysis with the R statistical language. With a comparison of both well-established and cutting-edge propensity score methods, the text highlights where solid guidelines exist to support best practices and where there is scarcity of research. Readers will find that this scaffolded approach to R and the book’s free online resources help them apply the text’s concepts to the analysis of their own data.
Propensity Score Analysis
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Author : Wei Pan
language : en
Publisher: Guilford Publications
Release Date : 2015-04-07
Propensity Score Analysis written by Wei Pan and has been published by Guilford Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-07 with Psychology categories.
This book is designed to help researchers better design and analyze observational data from quasi-experimental studies and improve the validity of research on causal claims. It provides clear guidance on the use of different propensity score analysis (PSA) methods, from the fundamentals to complex, cutting-edge techniques. Experts in the field introduce underlying concepts and current issues and review relevant software programs for PSA. The book addresses the steps in propensity score estimation, including the use of generalized boosted models, how to identify which matching methods work best with specific types of data, and the evaluation of balance results on key background covariates after matching. Also covered are applications of PSA with complex data, working with missing data, controlling for unobserved confounding, and the extension of PSA to prognostic score analysis for causal inference. User-friendly features include statistical program codes and application examples. Data and software code for the examples are available at the companion website (www.guilford.com/pan-materials).
Secondary Analysis Of Electronic Health Records
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Author : MIT Critical Data
language : en
Publisher: Springer
Release Date : 2016-09-09
Secondary Analysis Of Electronic Health Records written by MIT Critical Data and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-09 with Medical categories.
This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.
Using Propensity Scores In Quasi Experimental Designs
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Author : William M. Holmes
language : en
Publisher: SAGE Publications
Release Date : 2013-06-10
Using Propensity Scores In Quasi Experimental Designs written by William M. Holmes and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-10 with Social Science categories.
Using Propensity Scores in Quasi-Experimental Designs, by William M. Holmes, examines how propensity scores can be used to reduce bias with different kinds of quasi-experimental designs and to fix or improve broken experiments. Requiring minimal use of matrix and vector algebra, the book covers the causal assumptions of propensity score estimates and their many uses, linking these uses with analysis appropriate for different designs. Thorough coverage of bias assessment, propensity score estimation, and estimate improvement is provided, along with graphical and statistical methods for this process. Applications are included for analysis of variance and covariance, maximum likelihood and logistic regression, two-stage least squares, generalized linear regression, and general estimation equations. The examples use public data sets that have policy and programmatic relevance across a variety of disciplines.
Propensity Score Analysis
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Author : Shenyang Guo
language : en
Publisher:
Release Date : 2020
Propensity Score Analysis written by Shenyang Guo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Anthropology categories.
Propensity score analysis (PSA) is a class of statistical methods developed for estimating treatment effects with nonexperimental data or causality analysis in general. Specifically, PSA offers an approach to program evaluation when randomized trials are infeasible or unethical, or when researchers need to assess treatment effects or causal effects from survey data, census data, administrative data, medical records data, or other types of observational data. In the social and health sciences, researchers often face a fundamental task of drawing conditioned causal inferences from quasi-experimental studies. Analytical challenges in making causal inferences can be addressed by a variety of statistical methods including a range of new approaches emerging in the field of PSA. This entry begins with a discussion of the counterfactual framework and two related assumptions. After providing the definition of propensity score and various methods to estimate the score, it discusses seven methods of applying the estimated propensity score in causal analysis, including greedy matching, optimal matching, propensity score subclassification, propensity score weighting, matching estimators, propensity score analysis with nonparametric regression, and propensity score analysis of categorical or continuous treatments. The entry ends with a discussion about the strengths and limitations of the propensity score approach, including the criticism about the method of nearest neighbor matching within a caliper. Selection bias due to unmeasured covariates remains a problem in PSA. The entry concludes that among various approaches, propensity score subclassification, propensity score weighting, and matching estimators are highly recommended.
Propensity Score Methods And Applications
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Author : Haiyan Bai
language : en
Publisher: SAGE Publications
Release Date : 2018-11-20
Propensity Score Methods And Applications written by Haiyan Bai and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-20 with Social Science categories.
A concise, introductory text, Propensity Score Methods and Applications describes propensity score methods (PSM) and how they are used to balance the distributions of observed covariates between treatment conditions as a means to reduce selection bias. This new QASS title specifically focuses on the procedures of implementing PSM for research in social sciences, instead of merely demonstrating the effectiveness of the method. Using succinct and approachable language to introduce the basic concepts of PSM, authors Haiyan Bai and M. H. Clark present basic concepts, assumptions, procedures, available software packages, and step-by-step examples for implementing PSM using real-world data, with exercises at the end of each chapter allowing readers to replicate examples on their own.
Methods In Social Epidemiology
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Author : J. Michael Oakes
language : en
Publisher: John Wiley & Sons
Release Date : 2006-05-11
Methods In Social Epidemiology written by J. Michael Oakes and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-05-11 with Medical categories.
Social epidemiology is the study of how social interactions—social norms, laws, institutions, conventia, social conditions and behavior—affect the health of populations. This practical, comprehensive introduction to methods in social epidemiology is written by experts in the field. It is perfectly timed for the growth in interest among those in public health, community health, preventive medicine, sociology, political science, social work, and other areas of social research. Topics covered are: Introduction: Advancing Methods in Social Epidemiology The History of Methods of Social Epidemilogy to 1965 Indicators of Socioeconomic Position Measuring and Analyzing 'Race' Racism and Racial Discrimination Measuring Poverty Measuring Health Inequalities A Conceptual Framework for Measuring Segregation and its Association with Population Outcomes Measures of Residential Community Contexts Using Census Data to Approximate Neighborhood Effects Community-based Participatory Research: Rationale and Relevance for Social Epidemiology Network Methods in Social Epidemiology Identifying Social Interactions: A Review, Multilevel Studies Experimental Social Epidemiology: Controlled Community Trials Propensity Score Matching Methods for Social Epidemiology Natural Experiments and Instrumental Variable Analyses in Social Epidemiology and Using Causal Diagrams to Understand Common Problems in Social Epidemiology. "Publication of this highly informative textbook clearly reflects the coming of age of many social epidemiology methods, the importance of which rests on their potential contribution to significantly improving the effectiveness of the population-based approach to prevention. This book should be of great interest not only to more advanced epidemiology students but also to epidemiologists in general, particularly those concerned with health policy and the translation of epidemiologic findings into public health practice. The cause of achieving a ‘more complete’ epidemiology envisaged by the editors has been significantly advanced by this excellent textbook." —Moyses Szklo, professor of epidemiology and editor-in-chief, American Journal of Epidemiology, Johns Hopkins University "Social epidemiology is a comparatively new field of inquiry that seeks to describe and explain the social and geographic distribution of health and of the determinants of health. This book considers the major methodological challenges facing this important field. Its chapters, written by experts in a variety of disciplines, are most often authoritative, typically provocative, and often debatable, but always worth reading." —Stephen W. Raudenbush, Lewis-Sebring Distinguished Service Professor, Department of Sociology, University of Chicago "The roadmap for a new generation of social epidemiologists. The publication of this treatise is a significant event in the history of the discipline." —Ichiro Kawachi, professor of social epidemiology, Department of Society, Human Development, and Health, Harvard University "Methods in Social Epidemiology not only illuminates the difficult questions that future generations of social epidemiologists must ask, it also identifies the paths they must boldly travel in the pursuit of answers, if this exciting interdisciplinary science is to realize its full potential. This beautifully edited volume appears at just the right moment to exert a profound influence on the field." —Sherman A. James, Susan B. King Professor of Public Policy Studies, professor of Community and Family Medicine, professor of African-American Studies, Duke University
Practical Propensity Score Methods Using R
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Author : Walter Leite
language : en
Publisher:
Release Date : 2017
Practical Propensity Score Methods Using R written by Walter Leite and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Quantitative research categories.
This practical book uses a step--by--step analysis of realistic examples to help students understand the theory and code for implementing propensity score analysis with the R statistical language. With a comparison of both well--established and cutting--edge propensity score methods, the text highlights where solid guidelines exist to support best practices and where there is scarcity of research. Readers will find that this scaffolded approach to R and the book's free online resources help them apply the text's concepts to the analysis of their own data.
Piecewise Propensity Score Analysis
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Author : Russell T. Warne
language : en
Publisher:
Release Date : 2018
Piecewise Propensity Score Analysis written by Russell T. Warne and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Faculty contributions categories.
Propensity score analysis is widely used for simulating random assignment in observational studies when true random assignment is not possible. In propensity score modeling, a number of covariates are used to estimate the probability that an individual will belong to 1 of 2 groups. Prospective participants are then matched on their probabilities of belonging to the 2 groups rather than on the exact set of covariate values (as in traditional matching methods). However, traditional propensity score analysis can only be used in studies with 2 groups, such as an experimental and a control group. In this article, we propose a new method called piecewise propensity score analysis (PPSA) for ordinal polytomous grouping variables. We compared PPSA with another method of conducting propensity score analysis with ordered categories, marginal mean weighting through stratification (MMW-S; Hong, 2010, 2012) in a 3 × 5 × 4 study across three model misspecification conditions, five matching methods, and four sample sizes (1,000, 5,000, 10,000, 21,753). We found no significant difference between PPSA and MMW-S methods across conditions. We recommend linear regression, simple mean difference, or propensity stratification methods for simulating causal inference. One common purpose of research is to determine the effect of an intervention on an outcome. The gold standard for determining effects is a randomized control trial. A randomized control trial controls for unmeasured variables that may affect the outcome by randomly placing participants in either a treatment or control condition. This works well in cases in which participants can be randomized, but in many cases it is impossible to do so. For example, assigning people to psychological conditions, race, or gender is impossible. To address this, many people use a procedure called propensity score matching (PSM). One current method that is used for PSM is marginal mean weighting through stratification. This analysis is complicated to conduct and suffers from other disadvantages. To address these concerns, we propose a new method to conduct PSM called piecewise propensity score analysis (PPSA). PPSA is a method that can assist researchers in identifying causal effects when random assignment is not possible. Creating methods to accurately determine the effect of interventions is extremely important in educational research, psychological research, and behavioral and cognitive science research. PPSA is a method that can support researchers and practitioners in trying to understand human processes in order to improve them.