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Author: Erinn M. Hade
Publisher:
ISBN:
Category :
Languages : en
Pages : 96
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Book Description
Abstract: In medical and public health research, many studies are observational. In these studies, the treatment is not randomly assigned to participants. Therefore, the dif- ferences in outcomes between treatment groups could be due to imbalances of char- acteristics that are related to the outcome of interest prior to the treatment. Herein we investigate how we can use propensity scores, the conditional probability of re- ceiving treatment given the observed information, to make valid causal inference in observational studies. Theoretical results for the bias are given for linear response models that use the propensity score as a linear covariate. The bias depends on the relationship between the propensity score, the treatment indicator and the functional form of the covariate. Various methods for estimation of the treatment effect are explored. We show that the bias is influenced by the overlap in the distributions and functional forms of the covariates. When the distributions of the covariates have substantial overlap between treated and control groups, matching does well in terms of bias. In the second half of our work, we continue to investigate propensity score meth- ods for causal inference in observational studies, however our focus turns to studies with multiple groups. These methods are motivated by an example from the Pre- maturity Prevention Clinic at The Ohio State University. Our innovation relies on matching triplets of patients, which includes one patient from each of our groups of interest (those treated on-time, those with delayed treatment, and those who never were treated with 17P). Within each of these triplets, we attempt to balance pre- treatment characteristics by two matching techniques. We investigate two match- ing algorithms via simulation. Theese simulation studies found that a sub-optimal ii matching approach will in most circumstances provide better overall matches, than a nearest-neighbor approach. We implement our sub-optimal triplet matching for our motivating data and provide some conclusions about these data and future work with these methods.
Author: Erinn M. Hade
Publisher:
ISBN:
Category :
Languages : en
Pages : 96
Get Book Here
Book Description
Abstract: In medical and public health research, many studies are observational. In these studies, the treatment is not randomly assigned to participants. Therefore, the dif- ferences in outcomes between treatment groups could be due to imbalances of char- acteristics that are related to the outcome of interest prior to the treatment. Herein we investigate how we can use propensity scores, the conditional probability of re- ceiving treatment given the observed information, to make valid causal inference in observational studies. Theoretical results for the bias are given for linear response models that use the propensity score as a linear covariate. The bias depends on the relationship between the propensity score, the treatment indicator and the functional form of the covariate. Various methods for estimation of the treatment effect are explored. We show that the bias is influenced by the overlap in the distributions and functional forms of the covariates. When the distributions of the covariates have substantial overlap between treated and control groups, matching does well in terms of bias. In the second half of our work, we continue to investigate propensity score meth- ods for causal inference in observational studies, however our focus turns to studies with multiple groups. These methods are motivated by an example from the Pre- maturity Prevention Clinic at The Ohio State University. Our innovation relies on matching triplets of patients, which includes one patient from each of our groups of interest (those treated on-time, those with delayed treatment, and those who never were treated with 17P). Within each of these triplets, we attempt to balance pre- treatment characteristics by two matching techniques. We investigate two match- ing algorithms via simulation. Theese simulation studies found that a sub-optimal ii matching approach will in most circumstances provide better overall matches, than a nearest-neighbor approach. We implement our sub-optimal triplet matching for our motivating data and provide some conclusions about these data and future work with these methods.
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: 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: 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: Donald B. Rubin
Publisher: Cambridge University Press
ISBN: 1139458507
Category : Mathematics
Languages : en
Pages : 5
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Book Description
Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted. This book presents a selection of Donald B. Rubin's research articles on matched sampling, from the early 1970s, when the author was one of the major researchers involved in establishing the field, to recent contributions to this now extremely active area. The articles include fundamental theoretical studies that have become classics, important extensions, and real applications that range from breast cancer treatments to tobacco litigation to studies of criminal tendencies. They are organized into seven parts, each with an introduction by the author that provides historical and personal context and discusses the relevance of the work today. A concluding essay offers advice to investigators designing observational studies. The book provides an accessible introduction to the study of matched sampling and will be an indispensable reference for students and researchers.
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: Douglas E. Faries
Publisher: SAS Press
ISBN: 9781607642275
Category : Medical care
Languages : en
Pages : 0
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Book Description
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: Paul R. Rosenbaum
Publisher: Springer Science & Business Media
ISBN: 1441912134
Category : Mathematics
Languages : en
Pages : 382
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Book Description
An observational study is an empiric investigation of effects caused by treatments when randomized experimentation is unethical or infeasible. Observational studies are common in most fields that study the effects of treatments on people, including medicine, economics, epidemiology, education, psychology, political science and sociology. The quality and strength of evidence provided by an observational study is determined largely by its design. Design of Observational Studies is both an introduction to statistical inference in observational studies and a detailed discussion of the principles that guide the design of observational studies. Design of Observational Studies is divided into four parts. Chapters 2, 3, and 5 of Part I cover concisely, in about one hundred pages, many of the ideas discussed in Rosenbaum’s Observational Studies (also published by Springer) but in a less technical fashion. Part II discusses the practical aspects of using propensity scores and other tools to create a matched comparison that balances many covariates. Part II includes a chapter on matching in R. In Part III, the concept of design sensitivity is used to appraise the relative ability of competing designs to distinguish treatment effects from biases due to unmeasured covariates. Part IV discusses planning the analysis of an observational study, with particular reference to Sir Ronald Fisher’s striking advice for observational studies, "make your theories elaborate." The second edition of his book, Observational Studies, was published by Springer in 2002.
Author: Gregory R. Hancock
Publisher: Routledge
ISBN: 1135172994
Category : Education
Languages : en
Pages : 449
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Book Description
Designed for reviewers of research manuscripts and proposals in the social and behavioral sciences, and beyond, this title includes chapters that address traditional and emerging quantitative methods of data analysis.
Author: William M. Holmes
Publisher: SAGE Publications
ISBN: 1483310817
Category : Social Science
Languages : en
Pages : 361
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Book Description
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.
