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 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 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 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 Statistical Techniques for Estimating Causal Effects in Biomedical Research written by Claudia Coscia Requena and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Causal inference methods are statistical techniques used to analyse the causal effect of a treatment/exposure on an outcome. Their use is increasing in the last decade, especially in the framework of observational studies where the no randomization of the treatment/exposure may lead to confounding bias. These methods present great advantages versus classic regression models due to their capability of reducing and controlling for confounding bias.This thesis begins with the use of known techniques applied in real clinical scenarios, second, a lack of developed statistical methods to estimate causal effects in complex epidemiological scenarios is noted. These findings support the main objective of this thesis, which is the development of causal inference methods to better understand and diagnose clinical and epidemiological outcomes. A comparison between the Propensity Score and classic regression models was made using an Intensive Care Unit database where it was shown that, in presence of confounding bias, Propensity Score performed better. Moreover, based on a systematic review and metaanalysis, causal estimates from Propensity Score and Randomized Controlled Trials were compared. It was observed that similar estimations were obtained in both approaches...

Download or read book Estimating Causal Treatment Effects Via the Propensity Score and Estimating Survival Distributions in Clinical Trials That Follow Two Stage Randomization Designs written by and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimation of treatment effects with causalinterpretation from obervational data is complicated by the fact thatexposure to treatment is confounded with subject characteristics. Thepropensity score, the probability of exposure to treatment conditionalon covariates, is the basis for two competing classes of approachesfor adjusting for confounding: methods based on stratification ofobservations by quantiles of estimated propensity scores, and methods based on weighting individual observations by weights depending onestimated propensity scores. We review these approaches andinvestigate their relative performance. Some clinical trials follow a design in which patientsare randomized to a primary therapy upon entry followed by anotherrandomization to maintenance therapy contingent upon diseaseremission. Ideally, analysis would allow different treatmentpolicies, i.e. combinations of primary and maintenance therapy ifspecified up-front, to be compared. Standard practice is to conductseparate analyses for the primary and follow-up treatments, which doesnot address this issue directly. We propose consistent estimators ofthe survival distribution and mean survival time for each treatmentpolicy in such two-stage studies and derive large sampleproperties. The methods are demonstrated on a leukemia clinical trialdata set and through simulation.

Download or read book How Much Compliance is Enough written by Scott F. Grey and published by . This book was released on 2013 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: A recent solution to estimating treatment efficacy in studies with non-compliance has been the development of complier averaged causal effects (CACE) estimates. Based on principal stratification, these models classify subjects who receive an adequate amount of the treatment as potential compliers and compares them to control subjects who have an equal probability of being classified as compliers if they had been randomized to treatment. No studies have systematically examined how sensitive CACE estimates are to different definitions of compliance. This study hypothesizes that incorrect definitions of compliance can bias CACE estimates and seeks to determine under what circumstances bias can occur.The standard CACE framework is extended to a partial compliance framework where there can be multiple principal strata of partial potential compliance and there is a true minimum partial potential compliance principal stratum where subjects receive the minimum treatment exposure necessary to have a relevant outcome effect. In this framework, subjects can be incorrectly classified as non-compliers and compliers. Mathematical investigations and numeric analysis suggest that when non-compliers are incorrectly classified as compliers, CACE estimates are minimally affected. On the other hand, when compliers are incorrectly classified as non-compliers, CACE estimates can be grossly inflated. These results remain when CACE estimates were calculated using the exclusion restriction or a covariate, when the exclusion restriction is true and when is false. Missing data, a common occurrence in research that is often related to noncompliance, was found to somewhat attenuate the amount of bias observed.These findings suggest that misclassifying true non-compliers as compliers might introduce a small amount of bias into CACE estimates, but that misclassifying true compliers as non-compliers may introduce a substantial amount of bias into CACE estimates. This divergence below or above the true partial compliance principal strata may provide researchers with a method of identifying the true partial compliance principal strata using sensitivity analysis. This approach was tested using data from a large cluster randomized field trial, and appeared to be able to provide an estimate of the true partial compliance minimum, but the derived estimate did not obtain statistical significance, making it of questionable value.

Download or read book Estimating Causal Effects written by Barbara Schneider and published by . This book was released on 2007 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explains the value of quasi-experimental techniques that can be used to approximate randomized experiments. The goal is to describe the logic of causal inference for researchers and policymakers who are not necessarily trained in experimental and quasi-experimental designs and statistical techniques.

Download or read book Targeted Maximum Likelihood Estimation of Treatment Effects in Randomized Controlled Trials and Drug Safety Analysis written by Kelly Moore and published by . This book was released on 2009 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: In most randomized controlled trials (RCTs), investigators typically rely on estimators of causal effects that do not exploit the information in the many baseline covariates that are routinely collected in addition to treatment and the outcome. Ignoring these covariates can lead to a significant loss is estimation efficiency and thus power. Statisticians have underscored the gain in efficiency that can be achieved from covariate adjustment in RCTs with a focus on problems involving linear models. Despite recent theoretical advances, there has been a reluctance to adjust for covariates based on two primary reasons; 1) covariate-adjusted estimates based on non-linear regression models have been shown to be less precise than unadjusted methods, and, 2) concern over the opportunity to manipulate the model selection process for covariate adjustment in order to obtain favorable results. This dissertation describes statistical approaches for covariate adjustment in RCTs using targeted maximum likelihood methodology for estimation of causal effects with binary and right-censored survival outcomes. Chapter 2 provides the targeted maximum likelihood approach to covariate adjustment in RCTs with binary outcomes, focusing on the estimation of the risk difference, relative risk and odds ratio. In such trials, investigators generally rely on the unadjusted estimate as the literature indicates that covariate-adjusted estimates based on logistic regression models are less efficient. The crucial step that has been missing when adjusting for covariates is that one must integrate/average the adjusted estimate over those covariates in order to obtain the population-level effect. Chapter 2 shows that covariate adjustment in RCTs using logistic regression models can be mapped, by averaging over the covariate(s), to obtain a fully robust and efficient estimator of the marginal effect, which equals a targeted maximum likelihood estimator. Simulation studies are provided that demonstrate that this targeted maximum likelihood method increases efficiency and power over the unadjusted method, particularly for smaller sample sizes, even when the regression model is misspecified. Chapter 3 applies the methodology presented in Chapter 3 to a sampled RCT dataset with a binary outcome to further explore the origin of the gains in efficiency and provide a criterion for determining whether a gain in efficiency can be achieved with covariate adjustment over the unadjusted method. This chapter demonstrates through simulation studies and the data analysis that not only is the relation between $R̂2$ and efficiency gain important, but also the presence of empirical confounding. Based on the results of these studies, a complete strategy for analyzing these type of data is formalized that provides a robust method for covariate adjustment while protecting investigators from misuse of these methods for obtaining favorable inference. Chapters 4 and 5 focus on estimation of causal effects with right-censored survival outcomes. Time-to-event outcomes are naturally subject to right-censoring due to early patient withdrawals. In chapter 4, the targeted maximum likelihood methodology is applied to the estimation of treatment specific survival at a fixed end-point in time. In chapter 5, the same methodology is applied to provide a competitor to the logrank test. The proposed covariate adjusted estimators, under no or uninformative censoring, do not require any additional parametric modeling assumptions, and under informative censoring, are consistent under consistent estimation of the censoring mechanism or the conditional hazard for survival. These targeted maximum likelihood estimators have two important advantages over the Kaplan-Meier and logrank approaches; 1) they exploit covariates to improve efficiency, and 2) they are consistent in the presence of informative censoring. These properties are demonstrated through simulation studies. Chapter 6 concludes with a summary of the preceding chapters and a discussion of future research directions.

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)

Download or read book Non compliance in Clinical Trials written by Peter Drew Merrill and published by . This book was released on 2015 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: All clinical trials must deal with protocol deviations that occur during the course of the study. One of the most important deviations is non-compliance to treatment assignment. Intention to treat (ITT) is the most commonly employed method to deal with non-compliance in a clinical trial; however, it provides biased estimates of the effect of receiving the treatment. Other methods such as per protocol (PP) and as treated (AT) provide alternatives to ITT. PP and AT, assume an all-or-nothing compliance situation. However, the possibility of being partially compliant to a treatment is common. We investigate possible approaches to incorporating partial compliance data into design and analysis of a clinical trial. We examine the practice of dichotomizing partial compliance in order to use PP, AT, and the instrumental variables (IV) methods. We show that, under assumptions favorable to the use of PP, AT, and IV, dichotomizing the partial compliance data provides biased estimates, reduces power, and in some cases inflates type I error rates. We also investigate the use of these methods within a factorial design trial, in which participants may experience increased non-compliance due to being randomized to multiple treatments simultaneously. We investigate three methods that use partial compliance data in a linear regression model as a covariate. We show that under certain assumptions, these methods will provide unbiased estimates and improve the power of a test of the treatment effect without inflating type I error. These methods may have reduced power or inflated type I error rates when the assumptions are not met. We developed a novel way to use compliance information in an on-going clinical trial to increase study power by utilizing sample size re-estimation (SSR) and internal pilot (IP) methods, using an estimate of average compliance in the study population. An IP is used to correct the negative effects of misspecifying the average compliance at the initial sample size estimation. We showed that this method can help a study maintain the desired level of power in the study. If compliance in the population is low, the necessary sample size may become quite large.

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 Matching Methods for Estimating Causal Effects Using Multiple Control Groups written by Elizabeth Anne Stuart and published by . This book was released on 2004 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Estimating Treatment Effect in the Presence of Noncompliance Measured with Error written by Leslie Anne Kenna and published by . This book was released on 2001 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Randomized Experiments for Causal Effects Under Privacy written by Manjusha Kancharla and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis addresses inference problems in randomized experiments where the assumption that participants are willing to share their (potentially sensitive) response to treatment with the investigator may not be valid. Randomized control trials (RCTs) have been the gold standard to estimate the average causal effect of a treatment on a response. However, when the true response to treatment is not available, RCTs cannot estimate the true causal effects. This thesis presents a simple, differentially private experimental design, one of the strongest notions of data privacy in computer science. Simply put, the data collected from the design proposed in this thesis is information-theoretically guaranteed to be genuinely anonymous; the same guarantee does not exist for a traditional RCT where an adversary can potentially identify individual patients based on data from the RCT. Critically, the proposed design's strong guarantee of data privacy enables individual-level data to be publicly shared for scientific replication. This thesis also uses works on non-compliance in experimental psychology to make our design robust against ``adversarial'' study participants who may distrust the investigator with their data and provide contaminated responses to intentionally bias the study results. Under the new design, this thesis proposes unbiased and asymptotically Normal estimators for the average treatment effect. Additionally, this thesis presents a doubly robust estimator that leverages pre-treatment covariates, if available, to improve efficiency. This thesis also offers a simple method to make our design robust against ``adversarial'' study participants even with small sample sizes. Through several simulation studies, this thesis demonstrates the asymptotic and finite sample behavior of the proposed estimation procedures. Finally, this thesis discusses an application of the proposed design in a setting where sensitive student data is collected. Specifically, we evaluate different modes of learning in online statistics courses at the University of Wisconsin-Madison while guaranteeing data privacy.

Download or read book Estimating Causal Direct and Indirect Effects in the Presence of Post treatment Confounders written by Yasemin Kisbu Sakarya and published by . This book was released on 2013 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: In investigating mediating processes, researchers usually use randomized experiments and linear regression or structural equation modeling to determine if the treatment affects the hypothesized mediator and if the mediator affects the targeted outcome. However, randomizing the treatment will not yield accurate causal path estimates unless certain assumptions are satisfied. Since randomization of the mediator may not be plausible for most studies (i.e., the mediator status is not randomly assigned, but self-selected by participants), both the direct and indirect effects may be biased by confounding variables. The purpose of this dissertation is (1) to investigate the extent to which traditional mediation methods are affected by confounding variables and (2) to assess the statistical performance of several modern methods to address confounding variable effects in mediation analysis. This dissertation first reviewed the theoretical foundations of causal inference in statistical mediation analysis, modern statistical analysis for causal inference, and then described different methods to estimate causal direct and indirect effects in the presence of two post-treatment confounders. A large simulation study was designed to evaluate the extent to which ordinary regression and modern causal inference methods are able to obtain correct estimates of the direct and indirect effects when confounding variables that are present in the population are not included in the analysis. Five methods were compared in terms of bias, relative bias, mean square error, statistical power, Type I error rates, and confidence interval coverage to test how robust the methods are to the violation of the no unmeasured confounders assumption and confounder effect sizes. The methods explored were linear regression with adjustment, inverse propensity weighting, inverse propensity weighting with truncated weights, sequential g-estimation, and a doubly robust sequential g-estimation. Results showed that in estimating the direct and indirect effects, in general, sequential g-estimation performed the best in terms of bias, Type I error rates, power, and coverage across different confounder effect, direct effect, and sample sizes when all confounders were included in the estimation. When one of the two confounders were omitted from the estimation process, in general, none of the methods had acceptable relative bias in the simulation study. Omitting one of the confounders from estimation corresponded to the common case in mediation studies where no measure of a confounder is available but a confounder may affect the analysis. Failing to measure potential post-treatment confounder variables in a mediation model leads to biased estimates regardless of the analysis method used and emphasizes the importance of sensitivity analysis for causal mediation analysis.

Download or read book Estimating Causal Effects of Treatments in Experimental and Observational Studies written by Donald B. Rubin and published by . This book was released on 1972 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: