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Author: Qian Guan
Publisher:
ISBN:
Category :
Languages : en
Pages : 99
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Book Description
Author: Qian Guan
Publisher:
ISBN:
Category :
Languages : en
Pages : 99
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Book Description
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: Tristan Zajonc
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Policies in health, education, and economics often unfold sequentially and adapt to changing conditions. Such time-varying treatments pose problems for standard program evaluation methods because intermediate outcomes are simultaneously pre-treatment confounders and post-treatment outcomes. This paper extends the Bayesian perspective on causal inference and optimal treatment to these types of dynamic treatment regimes. The unifying idea remains ignorable treatment assignment, which now sequentially includes selection on intermediate outcomes. I present methods to estimate the causal effect of arbitrary regimes, recover the optimal regime, and characterize the set of feasible outcomes under different regimes. I demonstrate these methods through an application to optimal student tracking in ninth and tenth grade mathematics. The proposed estimands characterize outcomes, mobility, equity, and efficiency under different tracking regimes. For the sample considered, student mobility under the status-quo regime is significantly below the optimal rate and existing policies reinforce between student inequality. An easy to implement optimal dynamic tracking regime, which promotes more students to honors in tenth grade, increases average final achievement 0.07 standard deviations above the status quo while lowering inequality; there is no binding equity-efficiency tradeoff. The proposed methods provide a flexible and principled approach to causal inference for time-varying treatments and optimal treatment choice under uncertainty.
Author: Judea Pearl
Publisher: Createspace Independent Publishing Platform
ISBN: 9781507894293
Category : Causation
Languages : en
Pages : 0
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Book Description
This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called "causal effects" or "policy evaluation") (2) queries about probabilities of counterfactuals, (including assessment of "regret," "attribution" or "causes of effects") and (3) queries about direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. -- p. 1.
Author: Institute of Medicine
Publisher: National Academies Press
ISBN: 0309171148
Category : Medical
Languages : en
Pages : 221
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Book Description
Clinical trials are used to elucidate the most appropriate preventive, diagnostic, or treatment options for individuals with a given medical condition. Perhaps the most essential feature of a clinical trial is that it aims to use results based on a limited sample of research participants to see if the intervention is safe and effective or if it is comparable to a comparison treatment. Sample size is a crucial component of any clinical trial. A trial with a small number of research participants is more prone to variability and carries a considerable risk of failing to demonstrate the effectiveness of a given intervention when one really is present. This may occur in phase I (safety and pharmacologic profiles), II (pilot efficacy evaluation), and III (extensive assessment of safety and efficacy) trials. Although phase I and II studies may have smaller sample sizes, they usually have adequate statistical power, which is the committee's definition of a "large" trial. Sometimes a trial with eight participants may have adequate statistical power, statistical power being the probability of rejecting the null hypothesis when the hypothesis is false. Small Clinical Trials assesses the current methodologies and the appropriate situations for the conduct of clinical trials with small sample sizes. This report assesses the published literature on various strategies such as (1) meta-analysis to combine disparate information from several studies including Bayesian techniques as in the confidence profile method and (2) other alternatives such as assessing therapeutic results in a single treated population (e.g., astronauts) by sequentially measuring whether the intervention is falling above or below a preestablished probability outcome range and meeting predesigned specifications as opposed to incremental improvement.
Author: Alex Dmitrienko
Publisher: SAS Institute
ISBN: 1635261449
Category : Computers
Languages : en
Pages : 455
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Book Description
Analysis of Clinical Trials Using SAS®: A Practical Guide, Second Edition bridges the gap between modern statistical methodology and real-world clinical trial applications. Tutorial material and step-by-step instructions illustrated with examples from actual trials serve to define relevant statistical approaches, describe their clinical trial applications, and implement the approaches rapidly and efficiently using the power of SAS. Topics reflect the International Conference on Harmonization (ICH) guidelines for the pharmaceutical industry and address important statistical problems encountered in clinical trials. Commonly used methods are covered, including dose-escalation and dose-finding methods that are applied in Phase I and Phase II clinical trials, as well as important trial designs and analysis strategies that are employed in Phase II and Phase III clinical trials, such as multiplicity adjustment, data monitoring, and methods for handling incomplete data. This book also features recommendations from clinical trial experts and a discussion of relevant regulatory guidelines. This new edition includes more examples and case studies, new approaches for addressing statistical problems, and the following new technological updates: SAS procedures used in group sequential trials (PROC SEQDESIGN and PROC SEQTEST) SAS procedures used in repeated measures analysis (PROC GLIMMIX and PROC GEE) macros for implementing a broad range of randomization-based methods in clinical trials, performing complex multiplicity adjustments, and investigating the design and analysis of early phase trials (Phase I dose-escalation trials and Phase II dose-finding trials) Clinical statisticians, research scientists, and graduate students in biostatistics will greatly benefit from the decades of clinical research experience and the ready-to-use SAS macros compiled in this book.
Author: Peter Spirtes
Publisher: Springer Science & Business Media
ISBN: 1461227488
Category : Mathematics
Languages : en
Pages : 551
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Book Description
This book is intended for anyone, regardless of discipline, who is interested in the use of statistical methods to help obtain scientific explanations or to predict the outcomes of actions, experiments or policies. Much of G. Udny Yule's work illustrates a vision of statistics whose goal is to investigate when and how causal influences may be reliably inferred, and their comparative strengths estimated, from statistical samples. Yule's enterprise has been largely replaced by Ronald Fisher's conception, in which there is a fundamental cleavage between experimental and non experimental inquiry, and statistics is largely unable to aid in causal inference without randomized experimental trials. Every now and then members of the statistical community express misgivings about this turn of events, and, in our view, rightly so. Our work represents a return to something like Yule's conception of the enterprise of theoretical statistics and its potential practical benefits. If intellectual history in the 20th century had gone otherwise, there might have been a discipline to which our work belongs. As it happens, there is not. We develop material that belongs to statistics, to computer science, and to philosophy; the combination may not be entirely satisfactory for specialists in any of these subjects. We hope it is nonetheless satisfactory for its purpose.
Author: George E. P. Box
Publisher: John Wiley & Sons
ISBN: 111803144X
Category : Mathematics
Languages : en
Pages : 610
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Book Description
Its main objective is to examine the application and relevance of Bayes' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a priori. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly noninformative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach.
Author: Olivier Pourret
Publisher: John Wiley & Sons
ISBN: 9780470994542
Category : Mathematics
Languages : en
Pages : 446
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Book Description
Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering. Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks. The book: Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model. Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations. Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees. Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user. Offers a historical perspective on the subject and analyses future directions for research. Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.
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.
