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Author: Trinetri Ghosh
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Average causal effect is often used to compare the treatments or interventions in both randomized and observational studies. It has a wide variety of applications in medical, natural, and social sciences, for example, psychology, political science, economics, and so on. Due to the increased availability of high-dimensional pre-treatment information sets, dimension reduction is a major methodological issue in observational studies to estimate the average causal effect of a non-randomized treatment. Often assumptions are made to ensure model identifiability and to establish theoretical guarantees for nuisance conditional models. But these assumptions can be less flexible. In the first work (Chapter 2), to estimate the average causal effect in an observational study, we use a semiparametric locally efficient dimension-reduction approach to assess the treatment assignment mechanisms and average responses in both the treated and the non-treated groups. We then integrate our results using imputation, inverse probability weighting, and doubly robust augmentation estimators. Doubly robust estimators are locally efficient, and imputation estimators are super-efficient when the response models are correct. To take advantage of both procedures, we introduce a shrinkage estimator that combines the two. The proposed estimators retain the double robustness property while improving on the variance when the response model is correct. We demonstrate the performance of these estimators using simulated experiments and a real data set on the effect of maternal smoking on baby birth weight. In the second work (Chapter 3), we implemented semiparametric efficient method in an emerging topic, precision medicine, an approach to tailoring disease prevention and treatment that takes into account individual variability in genes, environment, and lifestyle for each person. The goal of precision medicine is to deploy appropriate and optimal treatment based on the context of a patient's individual characteristics to maximize the clinical benefit. In this work, we propose a new modeling and estimation approach to select the optimal treatment regime from two different options through constructing a robust estimating equation. The method is protected against misspecification of the propensity score function or the outcome regression model for the non-treated group or the potential non-monotonic treatment difference model. Nonparametric smoothing and dimension reduction are incorporated to estimate the treatment difference model. We then identify the optimal treatment by maximizing the value function and established theoretical properties of the treatment assignment strategy. We illustrate the performance and effectiveness of our proposed estimators through extensive simulation studies and a real-world application to Huntington's disease patients. In the third work (Chapter 4), we aim to obtain optimal individualized treatment rules in the covariate-adjusted randomization clinical trial with many covariates. We model the treatment effect with an unspecified function of a single index of the covariates and leave the baseline response completely arbitrary. We devise a class of estimators to consistently estimate the treatment effect function and its associated index while bypassing the estimation of the baseline response, which is subject to the curse of dimensionality. We further develop inference tools to identify predictive covariates and isolate effective treatment regions. The usefulness of the methods is demonstrated in both simulations and a clinical data example.
Author: Trinetri Ghosh
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Average causal effect is often used to compare the treatments or interventions in both randomized and observational studies. It has a wide variety of applications in medical, natural, and social sciences, for example, psychology, political science, economics, and so on. Due to the increased availability of high-dimensional pre-treatment information sets, dimension reduction is a major methodological issue in observational studies to estimate the average causal effect of a non-randomized treatment. Often assumptions are made to ensure model identifiability and to establish theoretical guarantees for nuisance conditional models. But these assumptions can be less flexible. In the first work (Chapter 2), to estimate the average causal effect in an observational study, we use a semiparametric locally efficient dimension-reduction approach to assess the treatment assignment mechanisms and average responses in both the treated and the non-treated groups. We then integrate our results using imputation, inverse probability weighting, and doubly robust augmentation estimators. Doubly robust estimators are locally efficient, and imputation estimators are super-efficient when the response models are correct. To take advantage of both procedures, we introduce a shrinkage estimator that combines the two. The proposed estimators retain the double robustness property while improving on the variance when the response model is correct. We demonstrate the performance of these estimators using simulated experiments and a real data set on the effect of maternal smoking on baby birth weight. In the second work (Chapter 3), we implemented semiparametric efficient method in an emerging topic, precision medicine, an approach to tailoring disease prevention and treatment that takes into account individual variability in genes, environment, and lifestyle for each person. The goal of precision medicine is to deploy appropriate and optimal treatment based on the context of a patient's individual characteristics to maximize the clinical benefit. In this work, we propose a new modeling and estimation approach to select the optimal treatment regime from two different options through constructing a robust estimating equation. The method is protected against misspecification of the propensity score function or the outcome regression model for the non-treated group or the potential non-monotonic treatment difference model. Nonparametric smoothing and dimension reduction are incorporated to estimate the treatment difference model. We then identify the optimal treatment by maximizing the value function and established theoretical properties of the treatment assignment strategy. We illustrate the performance and effectiveness of our proposed estimators through extensive simulation studies and a real-world application to Huntington's disease patients. In the third work (Chapter 4), we aim to obtain optimal individualized treatment rules in the covariate-adjusted randomization clinical trial with many covariates. We model the treatment effect with an unspecified function of a single index of the covariates and leave the baseline response completely arbitrary. We devise a class of estimators to consistently estimate the treatment effect function and its associated index while bypassing the estimation of the baseline response, which is subject to the curse of dimensionality. We further develop inference tools to identify predictive covariates and isolate effective treatment regions. The usefulness of the methods is demonstrated in both simulations and a clinical data example.
Author: Bibhas Chakraborty
Publisher: Springer Science & Business Media
ISBN: 1461474280
Category : Medical
Languages : en
Pages : 220
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Book Description
Statistical Methods for Dynamic Treatment Regimes shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine. This volume demonstrates these methods with their conceptual underpinnings and illustration through analysis of real and simulated data. These methods are immediately applicable to the practice of personalized medicine, which is a medical paradigm that emphasizes the systematic use of individual patient information to optimize patient health care. This is the first single source to provide an overview of methodology and results gathered from journals, proceedings, and technical reports with the goal of orienting researchers to the field. The first chapter establishes context for the statistical reader in the landscape of personalized medicine. Readers need only have familiarity with elementary calculus, linear algebra, and basic large-sample theory to use this text. Throughout the text, authors direct readers to available code or packages in different statistical languages to facilitate implementation. In cases where code does not already exist, the authors provide analytic approaches in sufficient detail that any researcher with knowledge of statistical programming could implement the methods from scratch. This will be an important volume for a wide range of researchers, including statisticians, epidemiologists, medical researchers, and machine learning researchers interested in medical applications. Advanced graduate students in statistics and biostatistics will also find material in Statistical Methods for Dynamic Treatment Regimes to be a critical part of their studies.
Author: Saptarshi Chatterjee
Publisher:
ISBN: 9780438392007
Category : Biometry
Languages : en
Pages : 119
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Book Description
In chronic diseases such as cancer, physicians make multiple treatment decisions over the course of a patient's disease depending on his/her biological characteristics and accrued information. Essentially, the treatment rule at each decision point is a function which takes the patients' biomarker information, treatment and outcome history available up to that point as an input and returns the treatment choice as an output. In the single treatment setting, the optimal treatment decision can be obtained by a regression model on the mean outcome conditional on treatment and covariates, where the optimal treatment is the one that corresponds to the most desirable mean outcome. However, due to its overdependence on the outcome regression model, this method is heavily prone to model misspecification. Also, given data from an observational study, the usual regression method does not control for the confounding bias induced by the covariates affecting both treatment and outcomes. Causal inference provides a general framework to estimate the treatment causal effect by comparing the potential outcomes under each treatment group. However, for an individual patient, only one potential outcome is observed limiting the direct comparison of potential outcomes at the patient level. A handful number of methods have been proposed in the recent precision medicine literature where they employ semi-parametric estimation methods such as inverse probability weighting (IPWE) to predict the optimal treatment by maximizing a certain predefined value function. However, the likelihood based methods have received little attention in this area, partly due to making model assumptions. To fill this gap, in this dissertation, we develop two fully Bayesian semiparametric likelihood based methods to predict the optimal treatment for a new patient based on the treatment and covariate information from an observed group of patients. In the first approach (BayesG) we extend the idea of parametric g-formula to include a semiparametric mean function within a marginal structural model framework. In the second approach (PSBayes), we connect the treatment assignment mechanism to a missing data framework and build on the Penalized Spline of Propensity Prediction (PSPP) method in the missing data literature to develop a methodology to predict and compare the potential outcomes of the new patient. The posterior predictive potential outcome distribution is then analyzed to predict the optimal treatment. The performance of the proposed methodologies is illustrated in five different simulation studies covering a wide range of scenarios. Overall, the true specifications of inverse probability methods display comparable performance whereas the misspecified models perform poorly. In the additive mean function scenarios, PSBayes outperform all other methods in having higher accuracy in predicting true optimal treatments, whereas the inverse probability based methods show better performance in nonlinear mean function cases. In the presence of non-effect modifiers, the BayesG approach performs better than other methods. We conclude the dissertation by discussing the extension of our proposed methods to a dynamic treatment setting.
Author: Guanglei Hong
Publisher: John Wiley & Sons
ISBN: 1119030609
Category : Mathematics
Languages : en
Pages : 443
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Book Description
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.
Author: Michael R. Kosorok
Publisher: SIAM
ISBN: 1611974178
Category : Medical
Languages : en
Pages : 354
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Book Description
Personalized medicine is a medical paradigm that emphasizes systematic use of individual patient information to optimize that patient's health care, particularly in managing chronic conditions and treating cancer. In the statistical literature, sequential decision making is known as an adaptive treatment strategy (ATS) or a dynamic treatment regime (DTR). The field of DTRs emerges at the interface of statistics, machine learning, and biomedical science to provide a data-driven framework for precision medicine.? The authors provide a learning-by-seeing approach to the development of ATSs, aimed at a broad audience of health researchers. All estimation procedures used are described in sufficient heuristic and technical detail so that less quantitative readers can understand the broad principles underlying the approaches. At the same time, more quantitative readers can implement these practices. This book provides the most up-to-date summary of the current state of the statistical research in personalized medicine; contains chapters by leaders in the area from both the statistics and computer sciences fields; and also contains a range of practical advice, introductory and expository materials, and case studies.?
Author: Rongling Wu
Publisher: CRC Press
ISBN: 0429528663
Category : Mathematics
Languages : en
Pages : 288
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Book Description
Modern medicine is undergoing a paradigm shift from a "one-size-fits-all" strategy to a more precise patient-customized therapy and medication plan. While the success of precision medicine relies on the level of pharmacogenomic knowledge, dissecting the genetic mechanisms of drug response in a sufficient detail requires powerful computational tools. Quantitative Methods for Precision Medicine: Pharmacogenomics in Action presents the advanced statistical methods for mapping pharmacogenetic control by integrating pharmacokinetic and pharmacodynamic principles of drug-body interactions. Beyond traditional reductionist-based statistical genetic approaches, statistical formulization in this book synthesizes elements of multiple disciplines to infer, visualize, and track how pharmacogenes interact together as an intricate but well-coordinated system to mediate patient-specific drug response. Features: Functional and systems mapping models to characterize the genetic architecture of multiple medication processes Statistical methods for analyzing informative missing data in pharmacogenetic association studies Functional graph theory of inferring genetic interaction networks from association data Leveraging the concept of epistasis to capture its bidirectional, signed and weighted properties Modeling gene-induced cell-cell crosstalk and its impact on drug response A graph model of drug-drug interactions in combination therapies Critical methodological issues to improve pharmacogenomic research as the cornerstone of precision medicine This book is suitable for graduate students and researchers in the fields of biology, medicine, bioinformatics and drug design and delivery who are interested in statistical and computational modelling of biological processes and systems. It may also serve as a major reference for applied mathematicians, computer scientists, and statisticians who attempt to develop algorithmic tools for genetic mapping, systems pharmacogenomics and systems biology. It can be used as both a textbook and research reference. Professionals in pharmaceutical sectors who design drugs and clinical doctors who deliver drugs will also find it useful.
Author: Yichuan Zhao
Publisher: Springer Nature
ISBN: 3031506901
Category :
Languages : en
Pages : 545
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Book Description
Author: Anastasios A. Tsiatis
Publisher: CRC Press
ISBN: 1498769780
Category : Mathematics
Languages : en
Pages : 602
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Book Description
Dynamic Treatment Regimes: Statistical Methods for Precision Medicine provides a comprehensive introduction to statistical methodology for the evaluation and discovery of dynamic treatment regimes from data. Researchers and graduate students in statistics, data science, and related quantitative disciplines with a background in probability and statistical inference and popular statistical modeling techniques will be prepared for further study of this rapidly evolving field. A dynamic treatment regime is a set of sequential decision rules, each corresponding to a key decision point in a disease or disorder process, where each rule takes as input patient information and returns the treatment option he or she should receive. Thus, a treatment regime formalizes how a clinician synthesizes patient information and selects treatments in practice. Treatment regimes are of obvious relevance to precision medicine, which involves tailoring treatment selection to patient characteristics in an evidence-based way. Of critical importance to precision medicine is estimation of an optimal treatment regime, one that, if used to select treatments for the patient population, would lead to the most beneficial outcome on average. Key methods for estimation of an optimal treatment regime from data are motivated and described in detail. A dedicated companion website presents full accounts of application of the methods using a comprehensive R package developed by the authors. The authors’ website www.dtr-book.com includes updates, corrections, new papers, and links to useful websites.
Author: Mark J. van der Laan
Publisher: Springer Science & Business Media
ISBN: 1441997822
Category : Mathematics
Languages : en
Pages : 628
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Book Description
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.
Author: Chan Park (Ph.D.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Recently, there has been growing interest in studying causal effects under clustered settings where individual study units can be naturally grouped together. When study units are clustered, data from study units are likely to be dependent of each other and one's potential outcome is affected by others' treatment status; this phenomenon is known as interference in causal inference. The most well-studied type of interference is partial interference where study units are partitioned into non-overlapping clusters and interference only arises within units in the same cluster. Due to the dependencies among units, widely used methodologies to estimate causal effects and optimal treatment rules that are developed under independent and identically distributed data assumption may not be directly applicable in clustered settings. To this end, my research during the doctoral program focuses on estimation of causal effects and optimal treatment rules under partial interference setting. In particular, (i) my research lies in developing flexible, nonparametric methods to infer causal effects in dependent data and showing the optimality of these methods, usually in the form of semiparametric efficiency theory; (ii) my research focuses on partially identifying the causal effects in terms of bounds under a small set of assumptions, as well as demonstrating the statistical properties of the bounding approaches; and (iii) my research interest includes the optimal treatment regime under the presence of interference and dependencies among units using nonparametric methods. In this dissertation presents some of my previous works with additional discussions at the end.
