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Author: Michael William Johnson
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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.
Author: Michael William Johnson
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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.
Author: Kristopher C. Gardner
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 67
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Book Description
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.
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: José R. Zubizarreta
Publisher: CRC Press
ISBN: 1000850811
Category : Mathematics
Languages : en
Pages : 634
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Book Description
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.
Author: Guihua Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 24
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Book Description
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.
Author: Charles F. Manski
Publisher:
ISBN:
Category : Health planning
Languages : en
Pages : 74
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Book Description
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.
Author: Lin H. Taft
Publisher:
ISBN:
Category : Instrumental variables (Statistics)
Languages : en
Pages : 168
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Book Description
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.
Author: Yang Jiang
Publisher:
ISBN:
Category :
Languages : en
Pages : 154
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Book Description
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.
Author: Alberto Bisin
Publisher: Academic Press
ISBN: 0128162686
Category : Business & Economics
Languages : en
Pages : 1002
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Book Description
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
Author: Christopher Ryan Runyon
Publisher:
ISBN:
Category :
Languages : en
Pages : 310
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Book Description
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
