The Experts below are selected from a list of 99 Experts worldwide ranked by ideXlab platform
Markus Klein - One of the best experts on this subject based on the ideXlab platform.
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early maternal employment and children s vocabulary and inductive reasoning ability a dynamic approach
Child Development, 2018Co-Authors: Michael Kühhirt, Markus KleinAbstract:This study investigates the relationship between early maternal employment History and children’s vocabulary and inductive reasoning ability at age 5, drawing on longitudinal information on 2,200 children from the Growing Up in Scotland data. Prior research rarely addresses dynamics in maternal employment and the methodological ramifications of time-variant confounding. The present study proposes various measures to capture duration, timing, and stability of early maternal employment and uses inverse probability of treatment weighting to control for time-variant confounders that may partially mediate the effect of maternal employment on cognitive scores. The findings suggest only modest differences in the above ability measures between children with similar observed Covariate History but who have been exposed to very different patterns of early maternal employment.
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Early Maternal Employment and Children’s Vocabulary and Inductive Reasoning Ability: A Dynamic Approach
2017Co-Authors: Michael Kühhirt, Markus KleinAbstract:This study investigates the relationship between early maternal employment History and children’s vocabulary and inductive reasoning ability at age 5, drawing on longitudinal information on 2,200 children from the Growing Up in Scotland data. Prior research rarely addresses dynamics in maternal employment and the methodological ramifications of time-variant confounding. The present study proposes various measures to capture duration, timing, and stability of early maternal employment and uses inverse probability of treatment weighting to control for time-variant confounders that may partially mediate the effect of maternal employment on cognitive scores. The findings suggest only modest differences in the above ability measures between children with similar observed Covariate History but who have been exposed to very different patterns of early maternal employment.
Susan A. Murphy - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Treatment Regimes
Annual review of statistics and its application, 2014Co-Authors: Bibhas Chakraborty, Susan A. MurphyAbstract:A dynamic treatment regime consists of a sequence of decision rules, one per stage of intervention, that dictate how to individualize treatments to patients, based on evolving treatment and Covariate History. These regimes are particularly useful for managing chronic disorders and fit well into the larger paradigm of personalized medicine. They provide one way to operationalize a clinical decision support system. Statistics plays a key role in the construction of evidence-based dynamic treatment regimes—informing the best study design as well as efficient estimation and valid inference. Owing to the many novel methodological challenges this area offers, it has been growing in popularity among statisticians in recent years. In this article, we review the key developments in this exciting field of research. In particular, we discuss the sequential multiple assignment randomized trial designs, estimation techniques like Q-learning and marginal structural models, and several inference techniques designed to a...
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Inference for non-regular parameters in optimal dynamic treatment regimes
Statistical methods in medical research, 2009Co-Authors: Bibhas Chakraborty, Susan A. Murphy, Victor J. StrecherAbstract:A dynamic treatment regime is a set of decision rules, one per stage, each taking a patient’s treatment and Covariate History as input, and outputting a recommended treatment. In the estimation of the optimal dynamic treatment regime from longitudinal data, the treatment effect parameters at any stage prior to the last can be non-regular under certain distributions of the data. This results in biased estimates and invalid confidence intervals for the treatment effect parameters. In this article, we discuss both the problem of non-regularity, and available estimation methods. We provide an extensive simulation study to compare the estimators in terms of their ability to lead to valid confidence intervals under a variety of non-regular scenarios. Analysis of a data set from a smoking cessation trial is provided as an illustration.
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Structural Nested Mean Models for Assessing Time-Varying Effect Moderation
Biometrics, 2009Co-Authors: Daniel Almirall, Thomas R. Ten Have, Susan A. MurphyAbstract:This article considers the problem of assessing causal effect moderation in longitudinal settings in which treatment (or exposure) is time-varying and so are the Covariates said to moderate its effect. Intermediate Causal Effects that describe time-varying causal effects of treatment conditional on past Covariate History are introduced and considered as part of Robins’ Structural Nested Mean Model. Two estimators of the intermediate causal effects, and their standard errors, are presented and discussed: The first is a proposed 2-Stage Regression Estimator. The second is Robins’ G-Estimator. The results of a small simulation study that begins to shed light on the small versus large sample performance of the estimators, and on the bias-variance trade-off between the two estimators are presented. The methodology is illustrated using longitudinal data from a depression study.
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Structural Nested Mean Models for Assessing Time-Varying Effect Moderation
Biometrics, 2009Co-Authors: Daniel Almirall, Thomas R. Ten Have, Susan A. MurphyAbstract:This article considers the problem of assessing causal effect moderation in longitudinal settings in which treatment (or exposure) is time varying and so are the Covariates said to moderate its effect. Intermediate causal effects that describe time-varying causal effects of treatment conditional on past Covariate History are introduced and considered as part of Robins' structural nested mean model. Two estimators of the intermediate causal effects, and their standard errors, are presented and discussed: The first is a proposed two-stage regression estimator. The second is Robins' G-estimator. The results of a small simulation study that begins to shed light on the small versus large sample performance of the estimators, and on the bias-variance trade-off between the two estimators are presented. The methodology is illustrated using longitudinal data from a depression study.
Bibhas Chakraborty - One of the best experts on this subject based on the ideXlab platform.
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Estimating optimal shared-parameter dynamic regimens with application to a multistage depression clinical trial.
Biometrics, 2016Co-Authors: Bibhas Chakraborty, Erica E M Moodie, Palash Ghosh, A. John RushAbstract:A dynamic treatment regimen consists of decision rules that recommend how to individualize treatment to patients based on available treatment and Covariate History. In many scientific domains, these decision rules are shared across stages of intervention. As an illustrative example, we discuss STAR*D, a multistage randomized clinical trial for treating major depression. Estimating these shared decision rules often amounts to estimating parameters indexing the decision rules that are shared across stages. In this article, we propose a novel simultaneous estimation procedure for the shared parameters based on Q-learning. We provide an extensive simulation study to illustrate the merit of the proposed method over simple competitors, in terms of the treatment allocation matching of the procedure with the "oracle" procedure, defined as the one that makes treatment recommendations based on the true parameter values as opposed to their estimates. We also look at bias and mean squared error of the individual parameter-estimates as secondary metrics. Finally, we analyze the STAR*D data using the proposed method.
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Inference about the expected performance of a data-driven dynamic treatment regime.
Clinical trials (London England), 2014Co-Authors: Bibhas Chakraborty, Eric B. Laber, Ying-qi ZhaoAbstract:BackgroundA dynamic treatment regime (DTR) comprises a sequence of decision rules, one per stage of intervention, that recommends how to individualize treatment to patients based on evolving treatment and Covariate History. These regimes are useful for managing chronic disorders, and fit into the larger paradigm of personalized medicine. The Value of a DTR is the expected outcome when the DTR is used to assign treatments to a population of interest.PurposeThe Value of a data-driven DTR, estimated using data from a Sequential Multiple Assignment Randomized Trial, is both a data-dependent parameter and a non-smooth function of the underlying generative distribution. These features introduce additional variability that is not accounted for by standard methods for conducting statistical inference, for example, the bootstrap or normal approximations, if applied without adjustment. Our purpose is to provide a feasible method for constructing valid confidence intervals (CIs) for this quantity of practical intere...
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Dynamic Treatment Regimes
Annual review of statistics and its application, 2014Co-Authors: Bibhas Chakraborty, Susan A. MurphyAbstract:A dynamic treatment regime consists of a sequence of decision rules, one per stage of intervention, that dictate how to individualize treatments to patients, based on evolving treatment and Covariate History. These regimes are particularly useful for managing chronic disorders and fit well into the larger paradigm of personalized medicine. They provide one way to operationalize a clinical decision support system. Statistics plays a key role in the construction of evidence-based dynamic treatment regimes—informing the best study design as well as efficient estimation and valid inference. Owing to the many novel methodological challenges this area offers, it has been growing in popularity among statisticians in recent years. In this article, we review the key developments in this exciting field of research. In particular, we discuss the sequential multiple assignment randomized trial designs, estimation techniques like Q-learning and marginal structural models, and several inference techniques designed to a...
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Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m-Out-of-n Bootstrap Scheme
Biometrics, 2013Co-Authors: Bibhas Chakraborty, Eric B. Laber, Ying-qi ZhaoAbstract:ummary A dynamic treatment regime consists of a set of decision rules that dictate how to individualize treatment to patients based on available treatment and Covariate History. A common method for estimating an optimal dynamic treatment regime from data is Q-learning which involves nonsmooth operations of the data. This nonsmoothness causes standard asymptotic approaches for inference like the bootstrap or Taylor series arguments to breakdown if applied without correction. Here, we consider the m-out-of-n bootstrap for constructing confidence intervals for the parameters indexing the optimal dynamic regime. We propose an adaptive choice of m and show that it produces asymptotically correct confidence sets under fixed alternatives. Furthermore, the proposed method has the advantage of being conceptually and computationally much simple than competing methods possessing this same theoretical property. We provide an extensive simulation study to compare the proposed method with currently available inference procedures. The results suggest that the proposed method delivers nominal coverage while being less conservative than alternatives. The proposed methods are implemented in the qLearn R-package and have been made available on the Comprehensive R-Archive Network (http://cran.r-project.org/). Analysis of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study is used as an illustrative example.
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q learning for estimating optimal dynamic treatment rules from observational data
Canadian Journal of Statistics-revue Canadienne De Statistique, 2012Co-Authors: Erica E M Moodie, Bibhas Chakraborty, Michael S KramerAbstract:The area of dynamic treatment regimes (DTR) aims to make inference about adaptive, multistage decision-making in clinical practice. A DTR is a set of decision rules, one per interval of treatment, where each decision is a function of treatment and Covariate History that returns a recommended treatment. Q-learning is a popular method from the reinforcement learning literature that has recently been applied to estimate DTRs. While, in principle, Q-learning can be used for both randomized and observational data, the focus in the literature thus far has been exclusively on the randomized treatment setting. We extend the method to incorporate measured confounding Covariates, using direct adjustment and a variety of propensity score approaches. The methods are examined under various settings including non-regular scenarios. We illustrate the methods in examining the effect of breastfeeding on vocabulary testing, based on data from the Promotion of Breastfeeding Intervention Trial.
Michael Kühhirt - One of the best experts on this subject based on the ideXlab platform.
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early maternal employment and children s vocabulary and inductive reasoning ability a dynamic approach
Child Development, 2018Co-Authors: Michael Kühhirt, Markus KleinAbstract:This study investigates the relationship between early maternal employment History and children’s vocabulary and inductive reasoning ability at age 5, drawing on longitudinal information on 2,200 children from the Growing Up in Scotland data. Prior research rarely addresses dynamics in maternal employment and the methodological ramifications of time-variant confounding. The present study proposes various measures to capture duration, timing, and stability of early maternal employment and uses inverse probability of treatment weighting to control for time-variant confounders that may partially mediate the effect of maternal employment on cognitive scores. The findings suggest only modest differences in the above ability measures between children with similar observed Covariate History but who have been exposed to very different patterns of early maternal employment.
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Early Maternal Employment and Children’s Vocabulary and Inductive Reasoning Ability: A Dynamic Approach
2017Co-Authors: Michael Kühhirt, Markus KleinAbstract:This study investigates the relationship between early maternal employment History and children’s vocabulary and inductive reasoning ability at age 5, drawing on longitudinal information on 2,200 children from the Growing Up in Scotland data. Prior research rarely addresses dynamics in maternal employment and the methodological ramifications of time-variant confounding. The present study proposes various measures to capture duration, timing, and stability of early maternal employment and uses inverse probability of treatment weighting to control for time-variant confounders that may partially mediate the effect of maternal employment on cognitive scores. The findings suggest only modest differences in the above ability measures between children with similar observed Covariate History but who have been exposed to very different patterns of early maternal employment.
Song R. - One of the best experts on this subject based on the ideXlab platform.
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Proper Inference for Value Function in High-Dimensional Q-Learning for Dynamic Treatment Regimes
American Statistical Association, 2019Co-Authors: Zhu W., Zeng D., Song R.Abstract:Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients’ responses to previous treatments as well as Covariate History. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of nonregularity problems in the presence of nonrespondents who have zero-treatment effects, especially when the dimension of the tailoring variables is high. In this article, we propose a high-dimensional Q-learning (HQ-learning) to facilitate the inference of optimal values and parameters. The proposed method allows us to simultaneously estimate the optimal dynamic treatment regimes and select the important variables that truly contribute to the individual reward. At the same time, hard thresholding is introduced in the method to eliminate the effects of the nonrespondents. The asymptotic properties for the parameter estimators as well as the estimated optimal value function are then established by adjusting the bias due to thresholding. Both simulation studies and real data analysis demonstrate satisfactory performance for obtaining the proper inference for the value function for the optimal dynamic treatment regimes. Supplementary materials for this article are available online
