Robust Interval Estimation of a Treatment Effect in Observational Studies Using Propensity Score Matching PDF Download
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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: 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: Yuyang Zhang
Publisher:
ISBN:
Category : Biometry
Languages : en
Pages : 167
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Book Description
Observational studies provide a rich source of data for evaluating causal relationships. Appropriate statistical methods for causal inference should be developed to account for the non-randomized nature of observational studies. Matching design is commonly used to deal with this non-randomized issue as it is robust to the model misspecification. To goal of this work is to use the matching design to perform causal inference in population and subpopulation. Propensity score is a powerful tool for adjusting observed confounding bias when there are a large number of confounders. Relatively few studies have focused on whether the post-matching analysis should adjust for the matching structure when estimate the population treatment effect. In the first part of the thesis, we compare results under different strategies with and without the matching design for both continuous outcome and binary outcome and discuss whether the post-matching should take into account when the treatment effect is homogeneous. \cite{zhang2020accounting} However, treatment effects are likely to be different across different subpopulations, especially in a real-world problem. We then propose a non-parametric matching tree (MT) to tackle both confounding adjustment and subgroup identification at the same time by combining the machine learning methods with matching designs. We prove that it produces unbiased subpopulation treatment effect estimators. To evaluate the performance of the proposed method, we run extensive simulation studies to compare it with popular tree-based causal inference methods. We apply the proposed method to examine the impact of Tobramycin for the patients' first pseudomonas aeruginosa chronic infection in Cystic Fibrosis disease in the U.S. We finally discuss limitations and potential future works.
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: Taylor Renee Pressler
Publisher:
ISBN:
Category :
Languages : en
Pages : 24
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Book Description
Abstract: While randomized controlled trials (RCT) are considered the "gold standard" for clinical studies, the use of exclusion criteria may impact the external validity of the results. It is un- known whether estimators of effect size are biased by excluding a portion of the population. However, it may be possible to use data from observational studies to estimate a difference between the population average treatment effect (PATE) of the included and excluded por- tions of the population, the sample selection error (SSE). We propose an estimator for the SSE and use simulation to study its properties while considering a non-constant treatment effect. We find that a doubly robust estimator that uses both propensity scores and a model for the outcome generally outperforms an estimator that solely relies on the use of propensity scores, even when model elements are misspecified.
Author: Wei Pan
Publisher: Guilford Publications
ISBN: 1462519547
Category : Psychology
Languages : en
Pages : 418
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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: Sungur Gurel
Publisher:
ISBN:
Category :
Languages : en
Pages : 61
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Book Description
We investigated the performance of four different propensity score (PS) methods to reduce selection bias in estimates of the average treatment effect (ATE) in observational studies: inverse probability of treatment weighting (IPTW), truncated inverse probability of treatment weighting (TIPTW), optimal full propensity score matching (OFPSM), and propensity score stratification (PSS). We compared these methods in combination with three methods of standard error estimation: weighted least squares regression (WLS), Taylor series linearization (TSL), and jackknife (JK). We conducted a Monte Carlo Simulation study manipulating the number of subjects and the ratio of treated to total sample size. The results indicated that IPTW and OFPSM methods removed almost all of the bias while TIPTW and PSS removed about 90% of the bias. Some of TSL and JK standard errors were acceptable, some marginally overestimated, and some moderately overestimated. For the lower ratio of treated on sample sizes, all of the WLS standard errors were strongly underestimated, as designs get balanced, the underestimation gets less serious. Especially for the OFPSM, all of the TSL and JK standard errors were overestimated and WLS standard errors under estimated under all simulated conditions.
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: 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: Antonio Moreno García
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Matching on the propensity score allows to estimate treatment effects using randomized inference when randomized experimentation is not feasible, e.g., when we have observational data. A fundamental assumption for the obtained estimates to be unbiased is the ignorable treatment assignment assumption. This assumption requires that individuals are selected into treatment or control groups based only on observable covariates. However, this is often not the case, and in real applications we often encounter selection on unobservables that generates hidden bias. The objective of this work is to explore the consequences of having unobserved variables that affect treatment selection. We use simulated and real data to evaluate how different propensity score matching techniques perform when they are used in the estimation of a treatment effect in the presence of unobserved variables.
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.
