Treatment Effect Estimation with Propensity Score Matching PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Treatment Effect Estimation with Propensity Score Matching PDF full book. Access full book title Treatment Effect Estimation with Propensity Score Matching by Ricarda Schmidl. Download full books in PDF and EPUB format.
Author: Ricarda Schmidl
Publisher:
ISBN:
Category :
Languages : en
Pages : 174
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Book Description
Author: Ricarda Schmidl
Publisher:
ISBN:
Category :
Languages : en
Pages : 174
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Book Description
Author: Walter Leite
Publisher: SAGE Publications
ISBN: 1483313395
Category : Social Science
Languages : en
Pages : 225
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Book Description
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.
Author: Agency for Health Care Research and Quality (U.S.)
Publisher: Government Printing Office
ISBN: 1587634236
Category : Medical
Languages : en
Pages : 236
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Book Description
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: Shenyang Guo
Publisher: SAGE
ISBN: 1452235007
Category : Business & Economics
Languages : en
Pages : 449
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Book Description
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.
Author: Wei Pan
Publisher: Guilford Publications
ISBN: 1462519490
Category : Psychology
Languages : en
Pages : 417
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Book Description
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).
Author: MIT Critical Data
Publisher: Springer
ISBN: 3319437429
Category : Medical
Languages : en
Pages : 435
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Book Description
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.
Author: Scott F. Kosten
Publisher:
ISBN:
Category : Statistics
Languages : en
Pages : 236
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Book Description
Estimating the treatment effect between a treatment group and a control group in an observational study is a challenging problem in statistics. Without random assignment of subjects, there are likely to be differences between the treatment group and control group on a set of baseline covariates. If one of these baseline covariates is correlated to the response variable, then the difference in sample means between the groups is likely to be a biased estimate of the true treatment effect. Propensity score matching has become an increasingly popular strategy for reducing bias in estimates of the treatment effect. This reduction in bias is accomplished by identifying a subset of the original control group, which is similar to the treatment group in terms of the measured baseline covariates. Our research focused on the development of a new procedure that combines propensity score matching and a rank-based analysis of the general linear model. Our procedure was compared to several others in a Monte Carlo simulation study. Overall, our procedure produced highly efficient and robust confidence intervals for a treatment effect in an observational study. In addition to the Monte Carlo simulation study, our procedure and several other propensity score matching techniques were used to analyze two real world datasets for the presence of a treatment effect.
Author: Koichiro Niwa
Publisher: Springer
ISBN: 4431560718
Category : Medical
Languages : en
Pages : 327
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Book Description
This is the first textbook to focus on Aortopathy, a new clinical concept for a form of vasculopathy. The first section of the book starts from discussing general concept and history of Aortopathy, and then deals with its pathophysiology, manifestation, intrinsic factor, clinical implication, management and prevention. The second part closely looks at various disorders of the Aortopathy such as bicuspid aortic valve and coarctation of aorta. The book editors have published a lot of works on the topic and have been collecting relating data in the field of congenital heart disease for the past 20 years, thus present the book with confidence. The topic - an association of aortic pathophysiological abnormality, aortic dilation and aorto-left ventricular interaction - is getting more and more attention among cardiovascular physicians. This is the first book to refer for cardiologists, pediatric cardiologists, surgeons, ACHD specialists, etc. to acquire thorough knowledge on Aortopathy.
Author:
Publisher:
ISBN:
Category : Electronic books
Languages : en
Pages : 35
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Book Description
The propensity score (PS) is the probability of a subject receiving the treatment given the baseline covariates. People with the same propensity score tend to have the same distribution of covariates. Thus, propensity score related methods can be used to eliminate the systematic difference between treatment and control group so that improving the causal inferences in the observational study. In this project, a series of simulation studies are conducted to evaluate two widely used propensity score methods, matching and inverse probability of treatment weighting (IPTW), on their relative ability to estimate the treatment effect from non-randomized trials. One observes that the random forest based propensity score weighting can yield more promising treatment effect estimates compared with other PS methods. Besides that, simulated samples are also implemented to compare the performance of several matching methods on the balancing the covariates. It turns out that logistic regression based propensity score matching can reduce most of systematic differences between treatment and control group although it is not the top performer in the causal effect estimation. Finally, we illustrate the application of the propensity score methods discussed in the paper with an empirical example.
Author: Jordan H. Rickles
Publisher:
ISBN:
Category :
Languages : en
Pages : 13
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Book Description
The study is designed to demonstrate and test the utility of the proposed two-stage matching method compared to other analytic methods traditionally employed for multisite observational studies. More specifically, the study addresses the following research questions: (1) How do different specifications of the matching method influence covariate balance? (2) How do different specifications in the matching method influence inferences about treatment effect and effect heterogeneity? The different matching method specifications include differences in the propensity score model and whether a between-site match, within-site match, or two-stage matching process is used. The simulation results indicate that the two-stage matching method balances the desire for within-site covariate balance and the desire to retain as many treatment units in the analysis as possible. Relative to more straightforward matching methods, however, the two-stage matching method does not result in greater covariate balance nor less biased effect estimation. As a result, more straightforward methods that address the nested data structure--such as within-site matching or pooled matching with a random-intercept-and-slope propensity score model--might be preferable to the more complex two-stage matching method. These conclusions are based on a finite set of data generating conditions, with a small set of important confounders at both the unit and site level and a reasonable within-site sample size for matching. Future research should examine the performance of various propensity score model and matching methods under more extreme data conditions. (Contains 2 tables and 5 figures.).
