Download or read book Methods for Causal Inference in Randomized Trials with Multiple Versions of Control and Noncompliance with an Application to Behavioral Intervention Trials written by Scott Coggeshall and published by . This book was released on 2018 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Behavioral therapies are a class of interventions with a wide array of applications.Because of the complicated nature of these interventions, however, conducting randomized controlled trials of these interventions poses unique challenges compared to the classical blinded, placebo-controlled RCT. The primary issue is that RCTs of behavioral interventions often use treatment-as-usual (TAU) control groups, due to the lack of a feasible ”placebo” equivalent to the active intervention. As a result,control groups in these trials are typically heterogeneous with respect to the form of treatment received, making causal inference under the standard assumption of ”no multiple versions of treatment” no longer applicable. In this dissertation, we develop frameworks for causal inference in single-site and multi-site RCTs with multiple ver-sions of control due to the use of a TAU control group. We define causal estimands of interest based on a principle stratification approach. We show that these causal estimands are only partially identified with data from a single-site RCT, but can be identified under certain assumptions with data from a multi-site RCT. We then propose methods for performing inference for these causal estimands, either through bounding (in the case of partial identifiability) or point estimation (in the case of identifiability). Finally, we apply these methods to an RCT of a behavioral therapy intervention for children with autism. Additional work in this dissertation includes an examination of identifiability issues with methods for causal inference in RCTs with partial compliance, a tutorial for a Bayesian approach to binary non-compliance in RCTs, and a systematic review of behavioral interventions for children with autism.
Download or read book Methods for Estimating Causal Effects of Treatment in Randomized Controlled Trials with Simultaneous Provider and Subject Noncompliance written by Elisa Sheng and published by . This book was released on 2015 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: Subject noncompliance is a common problem in the analysis of randomized controlled trials (RCTs); with cognitive behavioral interventions, the addition of provider noncompliance further complicates making causal inference. As a motivating example, we consider a RCT of a Motivational Interviewing (MI)-based behavioral intervention for treating problem drug use. Treatment receipt depends on compliance of both a therapist (provider) and a patient (subject) where MI is `received' when the therapist adheres to the MI protocol and the patient actively participates in the intervention. However, therapists cannot be forced to follow protocol and patients cannot be forced to cooperate in an intervention. In this dissertation, we define causal estimands of interest based on a principal stratication framework, propose methods for estimating these causal estimands, and apply our proposals to a RCT of MI.
Download or read book Extending the Principal Stratification Method to Multi level Randomized Trials written by Jing Guo and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: ABSTRACT: The Principal Stratification method estimates a causal intervention effect by taking account of subjects' differences in participation, adherence or compliance. The current Principal Stratification method has been mostly used in randomized intervention trials with randomization at a single (individual) level with subjects who were randomly assigned to either intervention or control condition. However, randomized intervention trials have been conducted at group level instead of individual level in many scientific fields. This is so called "two-level randomization", where randomization is conducted at a group (second) level, above an individual level but outcome is often observed at individual level within each group. The incorrect inferences may result from the causal modeling if one only considers the compliance from individual level, but ignores it or be determine it from group level for a two-level randomized trial. The Principal Stratification method thus needs to be further developed to address this issue. To extend application of the Principal Stratification method, this research developed a new methodology for causal inferences in two-level intervention trials which principal stratification can be formed by both group level and individual level compliance. Built on the original Principal Stratification method, the new method incorporates a range of alternative methods to assess causal effects on a population when data on exposure at the group level are incomplete or limited, and are data at individual level. We use the Gatekeeper Training Trial, as a motivating example as well as for illustration. This study is focused on how to examine the intervention causal effect for schools that varied by level of adoption of the intervention program (Early-adopter vs. Later-adopter). In our case, the traditional Exclusion Restriction Assumption for Principal Stratification method is no longer hold. The results show that the intervention had a stronger impact on Later-Adopter group than Early-Adopter group for all participated schools. These impacts were larger for later trained schools than earlier trained schools. The study also shows that the intervention has a different impact on middle and high schools.
Download or read book Causality in a Social World written by Guanglei Hong and published by John Wiley & Sons. This book was released on 2015-06-09 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: Causality in a Social World introduces innovative new statistical research and strategies for investigating moderated intervention effects, mediated intervention effects, and spill-over effects using experimental or quasi-experimental data. The book uses potential outcomes to define causal effects, explains and evaluates identification assumptions using application examples, and compares innovative statistical strategies with conventional analysis methods. Whilst highlighting the crucial role of good research design and the evaluation of assumptions required for identifying causal effects in the context of each application, the author demonstrates that improved statistical procedures will greatly enhance the empirical study of causal relationship theory. Applications focus on interventions designed to improve outcomes for participants who are embedded in social settings, including families, classrooms, schools, neighbourhoods, and workplaces.
Download or read book Methods Matter written by Richard J. Murnane and published by Oxford University Press. This book was released on 2010-09-15 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Educational policy-makers around the world constantly make decisions about how to use scarce resources to improve the education of children. Unfortunately, their decisions are rarely informed by evidence on the consequences of these initiatives in other settings. Nor are decisions typically accompanied by well-formulated plans to evaluate their causal impacts. As a result, knowledge about what works in different situations has been very slow to accumulate. Over the last several decades, advances in research methodology, administrative record keeping, and statistical software have dramatically increased the potential for researchers to conduct compelling evaluations of the causal impacts of educational interventions, and the number of well-designed studies is growing. Written in clear, concise prose, Methods Matter: Improving Causal Inference in Educational and Social Science Research offers essential guidance for those who evaluate educational policies. Using numerous examples of high-quality studies that have evaluated the causal impacts of important educational interventions, the authors go beyond the simple presentation of new analytical methods to discuss the controversies surrounding each study, and provide heuristic explanations that are also broadly accessible. Murnane and Willett offer strong methodological insights on causal inference, while also examining the consequences of a wide variety of educational policies implemented in the U.S. and abroad. Representing a unique contribution to the literature surrounding educational research, this landmark text will be invaluable for students and researchers in education and public policy, as well as those interested in social science.
Download or read book Methodological Issues in AIDS Behavioral Research written by David G. Ostrow and published by Springer Science & Business Media. This book was released on 2006-04-11 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methodological problems have hampered researchers' efforts to understand and control AIDS since the beginning of the epidemic. This practical book addresses these problems by using actual health research case studies to develop strategies regarding design and sampling, measurement, and analysis and modeling issues. Researchers working on both biological and behavioral aspects of the disease will find this work a singularly effective tool to improve their study designs.
Download or read book Estimation and Testing Methods for Causal Inference with Interference written by Wu Han and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Causal inference is one of the core areas of research in modern data science that allows researchers to determine whether a specific intervention or treatment has an effect on an outcome. In its most basic form, causal inference is concerned with understanding the "cause and effect" relationship between variables. This requires going beyond correlation to understand whether changing one variable leads to a change in another. The gold standard for inferring causality is the randomized controlled trial, which randomly assigns subjects to a treatment or control group and compares outcomes. While randomized controlled trials provide us with data to do causal inference, the subsequent statistical analysis often relies on a key assumption known as the Stable Unit Treatment Value Assumption (SUTVA). This assumption states that the treatment of one unit (or individual) does not affect the outcome of another unit. However, in many real-world situations, this assumption does not hold, leading to what is called interference or a violation of SUTVA. Interference can occur in various contexts such as social networks, where the treatment of one person can influence the outcomes of others, or in marketplace, where treatment of one entity can impact other entities of same type. Understanding and handling interference is a critical and complex aspect of causal inference, and it necessitates more advanced methods to correctly estimate causal effects. This dissertation offers new methodologies and theoretical results to address key issues in causal inference with interference. Specifically, we develop inferential results for causal effect estimators in panel experiments under interference, introduce novel estimation methods for causal effects with network experiments and tackle the problem of detecting interference in online controlled experiments with increasing allocation.
Download or read book Intervening in Adolescent Problem Behavior written by Thomas J. Dishion and published by Guilford Press. This book was released on 2003-05-22 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a multilevel intervention and prevention program for at-risk adolescents and their families. Grounded in over 15 years of important clinical and developmental research, the Adolescent Transitions Program (ATP) has been nationally recognized as a best practice for strengthening families and reducing adolescent substance use and antisocial behavior. The major focus is to support parents' skills and motivation to reduce adolescent problem behavior and promote success. Spelling out the why, what, and how of this proactive, culturally informed intervention, the volume provides a solid scientific framework and all of the materials needed to implement the program in school or community settings. Included are illustrative case examples and an appendix featuring reproducible handouts and forms.
Download or read book Emerging Issues in Causal Inference for Intervention Trials written by Qi Long and published by . This book was released on 2005 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays in Causal Inference written by Raiden B. Hasegawa and published by . This book was released on 2019 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: In observational studies, identifying assumptions may fail, often quietly and without notice, leading to biased causal estimates. Although less of a concern in randomized trials where treatment is assigned at random, bias may still enter the equation through other means. This dissertation has three parts, each developing new methods to address a particular pattern or source of bias in the setting being studied. In the first part, we extend the conventional sensitivity analysis methods for observational studies to better address patterns of heterogeneous confounding in matched-pair designs. We illustrate our method with two sibling studies on the impact of schooling on earnings, where the presence of unmeasured, heterogeneous ability bias is of material concern. The second part develops a modified difference-in-difference design for comparative interrupted time series studies. The method permits partial identification of causal effects when the parallel trends assumption is violated by an interaction between group and history. The method is applied to a study of the repeal of Missouri's permit-to-purchase handgun law and its effect on firearm homicide rates. In the final part, we present a study design to identify vaccine efficacy in randomized control trials when there is no gold standard case definition. Our approach augments a two-arm randomized trial with natural variation of a genetic trait to produce a factorial experiment. The method is motivated by the inexact case definition of clinical malaria.
Download or read book Extensions of Randomization Based Methods for Causal Inference written by Joseph Jiazong Lee and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In randomized experiments, the random assignment of units to treatment groups justifies many of the traditional analysis methods for evaluating causal effects. Specifying subgroups of units for further examination after observing outcomes, however, may partially nullify any advantages of randomized assignment when data are analyzed naively. Some previous statistical literature has treated all post-hoc analyses homogeneously as entirely invalid and thus uninterpretable. Alternative analysis methods and the extent of the validity of such analyses remain largely unstudied. Here Chapter 1 proposes a novel, randomization-based method that generates valid post-hoc subgroup p-values, provided we know exactly how the subgroups were constructed. If we do not know the exact subgrouping procedure, our method may still place helpful bounds on the significance level of estimated effects. Chapter 2 extends the proposed methodology to generate valid posterior predictive p-values for partially post-hoc subgroup analyses, i.e., analyses that compare existing experimental data -- from which a subgroup specification is derived -- to new, subgroup-only data. Both chapters are motivated by pharmaceutical examples in which subgroup analyses played pivotal and controversial roles. Chapter 3 extends our randomization-based methodology to more general randomized experiments with multiple testing and nuisance unknowns. The results are valid familywise tests that are doubly advantageous, in terms of statistical power, over traditional methods. We apply our methods to data from the United States Job Training Partnership Act (JTPA) Study, where our analyses lead to different conclusions regarding the significance of estimated JTPA effects. In all chapters, we investigate the operating characteristics and demonstrate the advantages of our methods through series of simulations.
Download or read book Explanation in Causal Inference written by Tyler J. VanderWeele and published by Oxford University Press, USA. This book was released on 2015 with total page 729 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive examination of methods for mediation and interaction, VanderWeele's book is the first to approach this topic from the perspective of causal inference. Numerous software tools are provided, and the text is both accessible and easy to read, with examples drawn from diverse fields. The result is an essential reference for anyone conducting empirical research in the biomedical or social sciences.
Download or read book Journal of the American Statistical Association written by and published by . This book was released on 2009 with total page 896 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Causal Inference Outside of Randomized Trials with the Stability Controlled Quasi Experiment written by David Wulf and published by . This book was released on 2021 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Causal Inference with Non standard Experimental Designs written by Han Wu (Researcher in causal inference) and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decades have seen a comprehensive body of research dedicated to causal inference in conventional experimental designs. However, as technological innovations continue to foster a rapid influx of data across numerous fields, the datasets derived often exhibit new structures that stem from unconventional designs. The thesis at hand is centered around the development of methods for conducting causal inference, particularly when the design deviates from the standard, thereby making conventional methods inapplicable. Chapter 2 delves into the regression discontinuity design in cases where the running variable is a noisy measurement of a latent variable. We propose a novel design-based approach for estimation and inference. This approach proves effective when applied to a broad array of widely-used estimands. Chapter 3 explores adaptive experimentation in the context of delayed feedback. In subchapter 3.1, we extend Thompson sampling to the proportional hazard model and develop a method capable of overcoming challenges associated with vaccine trials. Subsequently, in subchapter 3.2, we study the behavior of Thompson sampling when delays are unrestricted, providing theoretical regret bounds and conducting extensive experiments. Chapter 4 investigates policy learning in scenarios involving multiple treatments or multiple outcomes. In subchapter 4.1, we propose methods for evaluating policies when cost constraints accompany multiple treatments. In subchapter 4.2, we introduce a personalized experimentation system that can learn interpretable policies from experimental data and is scalable for big datasets.
Download or read book The Elements of Joint Learning and Optimization in Operations Management written by Xi Chen and published by Springer Nature. This book was released on 2022-09-20 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines recent developments in Operations Management, and focuses on four major application areas: dynamic pricing, assortment optimization, supply chain and inventory management, and healthcare operations. Data-driven optimization in which real-time input of data is being used to simultaneously learn the (true) underlying model of a system and optimize its performance, is becoming increasingly important in the last few years, especially with the rise of Big Data.
Download or read book Developing a Protocol for Observational Comparative Effectiveness Research A User s Guide written by Agency for Health Care Research and Quality (U.S.) and published by Government Printing Office. This book was released on 2013-02-21 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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)
