Download or read book Statistical Methods for Studying Heterogeneous Treatment Effects with Instrumental Variables written by Michael William Johnson and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is a growing interest in estimating heterogeneous treatment effects in randomized and observational studies. However, most of the work relies on the assumption of ignorability, or no unmeasured confounding on the treatment effect. While instrumental variables (IV) are a popular technique to control for unmeasured confounding, there has been little research conducted to study heterogeneous treatment effects with the use of an IV. This dissertation introduces methods using an IV to discover novel subgroups, estimate their heterogeneous treatment effects, and identify individualized treatment rules (ITR) when ignorability is expected to be violated. In Chapter 2, we present a two-part algorithm to estimate heterogeneous treatment effects and detect novel subgroups using an IV with matching. The first part uses interpretable machine learning techniques, such as classification and regression trees, to discover potential effect modifiers. The second part uses closed testing to test for statistical significance of each effect modifier while strongly controlling the familywise error rate. We apply this method on the Oregon Health Insurance Experiment, estimating the effect of Medicaid on the number of days an individual's health does not impede their usual activities by using a randomized lottery as an instrument. In Chapter 3, we generalize methods to identify ITR using a binary IV to using multiple, discrete valued instruments, or equivalently, multilevel instruments. Several new problems arise when generalizing to multilevel instruments, requiring novel solutions. In particular, multilevel IV give rise to many latent subgroups that may experience heterogeneous treatment effects. Additionally, it may be unclear how to combine and compare the different levels of the IV to estimate treatment heterogeneity. We provide methods that use a prediction of the latent subgroup to identify optimal ITR, and methods to dynamically combine levels of the multilevel IV to estimate the heterogeneous treatment effects, effectively individualizing estimation of an ITR. Further, we provide and discuss necessary and sufficient conditions to identify an optimal ITR using a multilevel IV. We apply our methods to identify an ITR for two competing treatments, carotid endarterectomy and carotid artery stenting, on preventing stroke or death within 30 days of their index procedure.

Download or read book Statistical Methods for Assessing Treatment Effects for Observational Studies written by Kristopher C. Gardner and published by . This book was released on 2014 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt: Though randomized clinical (RCTs) trials are the gold standard for comparing treatments, they are often infeasible or exclude clinically important subjects, or generally represent an idealized medical setting rather than real practice. Observational data provide an opportunity to study practice-based evidence, but also present challenges for analysis. Traditional statistical methods which are suitable for RCTs may be inadequate for the observational studies. In this project, four of the most popular statistical methods for observational studies: ANCOVA, propensity score matching, regression with the propensity score as a covariate, and instrumental variables (IV) are investigated through application to MarketScan insurance claims data. Each of these methods is used to compare BMP versus autograft spinal surgeries for the outcomes length of stay, complications, and cost. Recommendations are made as to when each particular method may or may not be the optimal choice.

Download or read book An Instrumental Variable Tree Approach for Detecting Heterogeneous Treatment Effects in Observational Studies written by Guihua Wang and published by . This book was released on 2018 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop a technique that incorporates the instrumental variable method into a causal tree to correct for potential endogeneity biases in heterogeneous treatment effect analysis using observational studies. The resulting instrumental variable tree approach partitions subjects into subgroups with similar treatment effects within subgroups and different treatment effects across subgroups. The estimated treatment effects are asymptotically consistent under very general assumptions. Using simulated data, we show that our approach has better coverage rates and smaller mean-squared errors than the conventional causal tree, and that a forest constructed using instrumental variable trees has better accuracy and interpretability than the generalized random forest.

Download or read book Handbook of Matching and Weighting Adjustments for Causal Inference written by José R. Zubizarreta and published by CRC Press. This book was released on 2023-04-11 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures.

Download or read book Heterogeneous Treatment Effect Estimation in Observational Studies Using Tree based Methods written by Yuyang Zhang and published by . This book was released on 2020 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Using Studies of Treatment Response to Inform Treatment Choice in Heterogeneous Populations written by Charles F. Manski and published by . This book was released on 2000 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: An important practical objective of empirical studies of treatment response is to provide decision makers with information useful in choosing treatments. Often the decision maker is a planner who must choose treatments for the members of a heterogeneous population; for example, a physician may choose medical treatments for a population of patients. Studies of treatment response cannot provide all the information that planners would like to have as they choose treatments, but researchers can be of service by addressing several questions: How should studies be designed in order to be most informative? How should studies report their findings so as to be most useful in decision making? How should planners utilize the information that studies provide? This paper addresses aspects of these broad questions, focusing on pervasive problems of identification and statistical inference that arise when studying treatment response.

Download or read book Statistical Methods to Study Heterogeneity of Treatment Effects written by Lin H. Taft and published by . This book was released on 2016 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Randomized studies are designed to estimate the average treatment effect (ATE) of an intervention. Individuals may derive quantitatively, or even qualitatively, different effects from the ATE, which is called the heterogeneity of treatment effect. It is important to detect the existence of heterogeneity in the treatment responses, and identify the different sub-populations. Two corresponding statistical methods will be discussed in this talk: a hypothesis testing procedure and a mixture-model based approach. The hypothesis testing procedure was constructed to test for the existence of a treatment effect in sub-populations. The test is nonparametric, and can be applied to all types of outcome measures. A key innovation of this test is to build stochastic search into the test statistic to detect signals that may not be linearly related to the multiple covariates. Simulations were performed to compare the proposed test with existing methods. Power calculation strategy was also developed for the proposed test at the design stage. The mixture-model based approach was developed to identify and study the sub-populations with different treatment effects from an intervention. A latent binary variable was used to indicate whether or not a subject was in a sub-population with average treatment benefit. The mixture-model combines a logistic formulation of the latent variable with proportional hazards models. The parameters in the mixture-model were estimated by the EM algorithm. The properties of the estimators were then studied by the simulations. Finally, all above methods were applied to a real randomized study in a low ejection fraction population that compared the Implantable Cardioverter Defibrillator (ICD) with conventional medical therapy in reducing total mortality.

Download or read book Topics in Statistical Inference for Treatment Effects written by Yang Jiang and published by . This book was released on 2017 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis unites three papers discussing different approaches for estimating treatment effects, either in observational study or randomized trial. The first paper presents an approach to sensitivity analysis for the instrumental variable (IV) method, which examines the sensitivity of inferences to violations of IV validity. Our approach is based on extending the Anderson-Rubin test and is robust to weak IVs. The second paper presents a unified R software ivmodel for analyzing instrumental variables with one endogenous variable. The package implements a general class of estimators, k-class estimators, and two confidence intervals that are fully robust to weak instruments. The package also provides power formulas. The sensitivity analysis discussed in the first paper is also included in the package. The third paper uses Hidden Markov Model to estimate the dynamic effects of lottery-based incentives towards patient's healthy behavior every day. The data is collected from randomized clinical trials.

Download or read book The Handbook of Historical Economics written by Alberto Bisin and published by Academic Press. This book was released on 2021-04-21 with total page 1002 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Historical Economics guides students and researchers through a quantitative economic history that uses fully up-to-date econometric methods. The book's coverage of statistics applied to the social sciences makes it invaluable to a broad readership. As new sources and applications of data in every economic field are enabling economists to ask and answer new fundamental questions, this book presents an up-to-date reference on the topics at hand. Provides an historical outline of the two cliometric revolutions, highlighting the similarities and the differences between the two Surveys the issues and principal results of the "second cliometric revolution" Explores innovations in formulating hypotheses and statistical testing, relating them to wider trends in data-driven, empirical economics

Download or read book Using Multisite Instrumental Variables to Estimate Treatment Effects and Treatment Effect Heterogeneity written by Christopher Ryan Runyon and published by . This book was released on 2020 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multisite randomized trials (MSTs) are an attractive research design to test the efficacy of an educational program at scale. Population models examining data from MSTs can provide information on the range of possible treatment effects that sites (such as schools) can expect from an educational program, even for those sites not included in the study. However, when some individuals at a site do not comply with their treatment assignment, conventional multilevel and meta-analytic estimation methods do not provide information on the effect of actually participating in the educational program. Instrumental variables (IV) is a method that can produce consistent estimates of the causal effect of participating in an educational program for those individuals that comply with their treatment assignment, an estimand called the complier-average treatment effect (CATE). IV methods for single-site trials are well understood and widely-used. Recently multisite IV models have been proposed to estimate the CATE and CATE heterogeneity across a population of sites, but the performance of these estimators has not been examined in a simulation study. Using Monte Carlo simulation, the current study examines the performance of three IV estimators and two conventional estimators in recovering the CATE and CATE heterogeneity under simulation conditions that resemble multisite trials of well-known educational programs

Download or read book Heterogeneous Treatment Effects Instrumental Variables Without Monotonicity written by and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Handbook of Research Methods and Applications in Empirical Microeconomics written by Hashimzade, Nigar and published by Edward Elgar Publishing. This book was released on 2021-11-18 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written in a comprehensive yet accessible style, this Handbook introduces readers to a range of modern empirical methods with applications in microeconomics, illustrating how to use two of the most popular software packages, Stata and R, in microeconometric applications.

Download or read book Design of Observational Studies written by Paul R. Rosenbaum and published by Springer Nature. This book was released on 2020-07-13 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition of 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. 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 organized into five parts. Chapters 2, 3, and 5 of Part I cover concisely 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, and includes an updated 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 is new to this edition; it discusses evidence factors and the computerized construction of more than one comparison group. Part V 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." This new edition features updated exploration of causal influence, with four new chapters, a new R package DOS2 designed as a companion for the book, and discussion of several of the latest matching packages for R. In particular, DOS2 allows readers to reproduce many analyses from Design of Observational Studies.

Download or read book Estimating Person centered Treatment PeT Effects Using Instrumental Variables written by Anirban Basu (Professor of health economics) and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper builds on the methods of local instrumental variables developed by Heckman and Vytlacil (1999, 2001, 2005) to estimate person-centered treatment (PeT) effects that are conditioned on the person's observed characteristics and averaged over the potential conditional distribution of unobserved characteristics that lead them to their observed treatment choices. PeT effects are more individualized than conditional treatment effects from a randomized setting with the same observed characteristics. PeT effects can be easily aggregated to construct any of the mean treatment effect parameters and, more importantly, are well-suited to comprehend individual-level treatment effect heterogeneity. The paper presents the theory behind PeT effects, studies their finite-sample properties using simulations and presents a novel analysis of treatment evaluation in health care.

Download or read book Statistical Methods for Learning Patients Heterogeneity and Treatment Effects to Achieve Precision Medicine written by Tianchen Xu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The burgeoning adoption of modern technologies provides a great opportunity for gathering multiple modalities of comprehensive personalized data on individuals. The thesis aims to address statistical challenges in analyzing these data, including patient-specific biomarkers, digital phenotypes and clinical data available from the electronic health records (EHRs) linked with other data sources to achieve precision medicine. The first part of the thesis introduces a dimension reduction method of microbiome data to facilitate subsequent analysis such as regression and clustering. We adopt the proposed zero-inflated Poisson factor analysis (ZIPFA) model on the Oral Infections, Glucose Intolerance and Insulin Resistance Study (ORIGINS) and provide valuable insights into the relation between subgingival microbiome and periodontal disease. The second part focuses on modeling the intensive longitudinal digital phenotypes collected by mobile devices. We develop a method based on a generalized state-space model to estimate the latent process of patient's health status.

Download or read book The Economics of Artificial Intelligence written by Ajay Agrawal and published by University of Chicago Press. This book was released on 2024-03-05 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.

Download or read book Essays in Honor of Jerry Hausman written by Badi H. Baltagi and published by Emerald Group Publishing. This book was released on 2012-12-17 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aims to annually publish original scholarly econometrics papers on designated topics with the intention of expanding the use of developed and emerging econometric techniques by disseminating ideas on the theory and practice of econometrics throughout the empirical economic, business and social science literature.