Download or read book Causal Inference for Randomized Trials with Noncompliance written by Jing Cheng and published by . This book was released on 2006 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Causal Inference in Randomized Trials with Noncompliance written by Yasutaka Chiba and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
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of treatment received, making causal inference under the standard assumption of ”no multiple versions of treatment” no longer applicable.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 Causal Modelling of Survival Data with Informative Noncompliance written by Lang'O Taabu Odondi and published by . This book was released on 2011 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Noncompliance to treatment allocation is likely to complicate estimation of causal effects in clinical trials. The ubiquitous nonrandom phenomenon of noncompliance renders per-protocol and as- treated analyses or even simple regression adjustments for noncompliance inadequate for causal inference. For survival data, several specialist methods have been developed when noncompliance is related to risk. The Causal Accelerated Life Model (CALM) allows time-dependent departures from randomized treatment in either arm and relates each observed event time to a potential event time that would have been observed if the control treatment had been given throughout the trial. Alternatively, the structural Proportional Hazards (C-Prophet) model accounts for all-or-nothing noncompliance in the treatment arm only while the CHARM estimator allows time-dependent departures from randomized treatment by considering survival outcome as a sequence of binary outcomes to provide an 'approximate' overall hazard ratio estimate which is adjusted for compliance. The problem of efficacy estimation is compounded for two-active treatment trials (additional noncompliance) where the ITT estimate provides a biased estimator for the true hazard ratio even under homogeneous treatment effects assumption. Using plausible arm-specific predictors of compliance, principal stratification methods can be applied to obtain principal effects for each stratum. The present work applies the above methods to data from the Esprit trials study which was conducted to ascertain whether or not unopposed oestrogen (hormone replacement therapy - HRT) reduced the risk of further cardiac events in postmenopausal women who survive a first myocardial infarction. We use statistically designed simulation studies to evaluate the performance of these methods in terms of bias and 95% confidence interval coverage. We also apply a principal stratification method to adjust for noncompliance in two treatment arms trial originally developed for binary data for survival analysis in terms of causal risk ratio. In a Bayesian framework, we apply the method to Esprit data to account for noncompliance in both treatment arms and estimate principal effects. We apply statistically designed simulation studies to evaluate the performance of the method in terms of bias in the causal effect estimates for each stratum. ITT analysis of the Esprit data showed the effects of taking HRT tablets was not statistically significantly different from placebo for both all cause mortality and myocardial reinfarction outcomes. Average compliance rate for HRT treatment was 43% and compliance rate decreased as the study progressed. CHARM and C-Prophet methods produced similar results but CALM performed best for Esprit: suggesting HRT would reduce risk of death by 50%. Simulation studies comparing the methods suggested that while both C-Prophet and CHARM methods performed equally well in terms of bias, the CALM method performed best in terms of both bias and 95% confidence interval coverage albeit with the largest RMSE. The principal stratification method failed for the Esprit study possibly due to the strong distribution assumption implicit in the method and lack of adequate compliance information in the data which produced large 95% credible intervals for the principal effect estimates. For moderate value of sensitivity parameter, principal stratification results suggested compliance with HRT tablets relative to placebo would reduce risk of mortality by 43% among the most compliant. Simulation studies on performance of this method showed narrower corresponding mean 95% credible intervals corresponding to the the causal risk ratio estimates for this subgroup compared to other strata. However, the results were sensitive to the unknown sensitivity parameter.
Download or read book Complications in Causal Inference Incorporating Information Observed After Treatment is Assigned written by David Allan Watson and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Randomized experiments are the gold standard for inferring causal effects of treatments. However, complications often arise in randomized experiments when trying to incorporate additional information that is observed after the treatment has been randomly assigned. The principal stratification framework has provided clarity to these problems by explicitly considering the potential outcomes of all information that is observed after treatment is randomly assigned. Principal stratification is a powerful general framework, but it is best understood in the context of specific applied problems (e.g., non-compliance in experiments and "censoring due to death" in clinical trials). This thesis considers three examples of the principal stratification framework, each focusing on different aspects of statistics and causal inference.
Download or read book Causal Inference in Statistics Social and Biomedical Sciences written by Guido W. Imbens and published by Cambridge University Press. This book was released on 2015-04-06 with total page 647 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.
Download or read book Estimating Causal Treatment Effect in Randomized Clinical Trials with Noncompliance and Outcome Nonresponse written by Leslie Taylor and published by . This book was released on 2008 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistical Causal Inferences and Their Applications in Public Health Research written by Hua He and published by Springer. This book was released on 2016-10-26 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate). It is particularly relevant for students pursuing degrees in statistics, biostatistics, and computational biology. Researchers and data analysts in public health and biomedical research will also find this book to be an important reference.
Download or read book Estimation of Causal Parameters in Randomized Clinical Trials with Noncompliance and Missing Data written by Richard Wang and published by . This book was released on 2003 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Randomization Based Inference for Treatment Modifying Factors in Randomized Trials with Noncompliance written by Pasi Korhonen and published by . This book was released on 1998 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Causal Inference in Python written by Matheus Facure and published by "O'Reilly Media, Inc.". This book was released on 2023-07-14 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: How many buyers will an additional dollar of online marketing bring in? Which customers will only buy when given a discount coupon? How do you establish an optimal pricing strategy? The best way to determine how the levers at our disposal affect the business metrics we want to drive is through causal inference. In this book, author Matheus Facure, senior data scientist at Nubank, explains the largely untapped potential of causal inference for estimating impacts and effects. Managers, data scientists, and business analysts will learn classical causal inference methods like randomized control trials (A/B tests), linear regression, propensity score, synthetic controls, and difference-in-differences. Each method is accompanied by an application in the industry to serve as a grounding example. With this book, you will: Learn how to use basic concepts of causal inference Frame a business problem as a causal inference problem Understand how bias gets in the way of causal inference Learn how causal effects can differ from person to person Use repeated observations of the same customers across time to adjust for biases Understand how causal effects differ across geographic locations Examine noncompliance bias and effect dilution
Download or read book Targeted Learning written by Mark J. van der Laan and published by Springer Science & Business Media. This book was released on 2011-06-17 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt: The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.
Download or read book Inference about Treatment Effects in Randomized Trials with Death Non compliance and Missing Data written by Rachel E. Whitaker and published by . This book was released on 2009 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Microeconometrics written by Steven Durlauf and published by Springer. This book was released on 2016-06-07 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: Specially selected from The New Palgrave Dictionary of Economics 2nd edition, each article within this compendium covers the fundamental themes within the discipline and is written by a leading practitioner in the field. A handy reference tool.
Download or read book The Prevention and Treatment of Missing Data in Clinical Trials written by National Research Council and published by National Academies Press. This book was released on 2010-12-21 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: Randomized clinical trials are the primary tool for evaluating new medical interventions. Randomization provides for a fair comparison between treatment and control groups, balancing out, on average, distributions of known and unknown factors among the participants. Unfortunately, these studies often lack a substantial percentage of data. This missing data reduces the benefit provided by the randomization and introduces potential biases in the comparison of the treatment groups. Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation. And in some studies, some or all of data collection ceases when participants discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable. The Prevention and Treatment of Missing Data in Clinical Trials concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations, the book offers more detailed recommendations on the conduct of clinical trials and techniques for analysis of trial data.
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.
