The Experts below are selected from a list of 23373 Experts worldwide ranked by ideXlab platform
James D Stamey - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Sample Size Determination for cost-effectiveness studies with censored data.
PloS one, 2018Co-Authors: Daniel P. Beavers, James D StameyAbstract:Cost-effectiveness models are commonly utilized to determine the combined clinical and economic impact of one treatment compared to another. However, most methods for Sample Size Determination of cost-effectiveness studies assume fully observed costs and effectiveness outcomes, which presents challenges for survival-based studies in which censoring exists. We propose a Bayesian method for the design and analysis of cost-effectiveness data in which costs and effectiveness may be censored, and the Sample Size is approximated for both power and assurance. We explore two parametric models and demonstrate the flexibility of the approach to accommodate a variety of modifications to study assumptions.
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Bayesian Sample Size Determination in Two-Sample Poisson Models
Biometrics & Biostatistics International Journal, 2015Co-Authors: R. Sides, David Kahle, James D StameyAbstract:Sample Size Determination is a vital part of clinical studies where cost and safety concerns lead to greater importance of not using more subjects and resources than are required. The Bayesian approach to Sample Size Determination has the advantages of being able to use prior data and expert opinion to possibly reduce the total Sample Size while also acknowledging all uncertainty at the design stage. We apply a Bayesian decision theoretic approach to the problem of comparing two Poisson rates and find the required Sample Size to obtain a desired power while controlling the Type I error rate.
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Bayesian Sample Size Determination for a clinical trial with correlated continuous and binary outcomes.
Journal of biopharmaceutical statistics, 2013Co-Authors: James D Stamey, Fanni Natanegara, John W. SeamanAbstract:In clinical trials, multiple outcomes are often collected in order to simultaneously assess effectiveness and safety. We develop a Bayesian procedure for determining the required Sample Size in a regression model where a continuous efficacy variable and a binary safety variable are observed. The Sample Size Determination procedure is simulation based. The model accounts for correlation between the two variables. Through examples we demonstrate that savings in total Sample Size are possible when the correlation between these two variables is sufficiently high.
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Bayesian Sample Size Determination for binary regression with a misclassified covariate and no gold standard
Computational Statistics & Data Analysis, 2012Co-Authors: Daniel P. Beavers, James D StameyAbstract:Covariate misclassification is a common problem in epidemiology, genetics, and other biomedical areas. Because this form of misclassification is known to bias estimators, accounting for it at the design stage is of high importance. In this paper, we extend on previous work applied to response misclassification by developing a Bayesian approach to Sample Size Determination for a covariate misclassification model with no gold standard. Our procedure considers both conditionally independent tests and tests in which dependence exists between classifiers. We specifically consider a Bayesian power criterion for the Sample Size Determination scheme, and we demonstrate the improvement in model power for our dual classifier approach compared to a naive single classifier approach.
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Bayesian Sample-Size Determination for two independent Poisson rates
Computer Methods and Programs in Biomedicine, 2010Co-Authors: Austin L. Hand, James D Stamey, Dean M YoungAbstract:Abstract: Because of the high cost and time constraints for clinical trials, researchers often need to determine the smallest Sample Size that provides accurate inferences for a parameter of interest. Although most experimenters have employed frequentist Sample-Size Determination methods, the Bayesian paradigm offers a wide variety of Sample-Size Determination methodologies. Bayesian Sample-Size Determination methods are becoming increasingly more popular in clinical trials because of their flexibility and easy interpretation inferences. Recently, Bayesian approaches have been used to determine the Sample Size of a single Poisson rate parameter in a clinical trial setting. In this paper, we extend these results to the comparison of two Poisson rates and develop methods for Sample-Size Determination for hypothesis testing in a Bayesian context. We have created functions in R to determine the parameters for the conjugate gamma prior and calculate the Sample Size for the average length criterion and average power methods. We also provide two examples that implement our Sample-Size Determination methods using clinical data.
Stefania Gubbiotti - One of the best experts on this subject based on the ideXlab platform.
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A decision‐theoretic approach to Sample Size Determination under several priors
Applied Stochastic Models in Business and Industry, 2016Co-Authors: Fulvio De Santis, Stefania GubbiottiAbstract:In this article, we consider Sample Size Determination for experiments in which estimation and design are performed by multiple parties. This problem has relevant applications in contexts involving adversarial decision makers, such as control theory, marketing, and drug testing. Specifically, we adopt a decision-theoretic perspective, and we assume that a decision on an unknown parameter of a statistical model involves two actors, Ee and Eo, who share the same data and loss function but not the same prior beliefs on the parameter. We also suppose that Ee has to use Eo's optimal action, and we finally assume that the experiment is planned by a third party, Pd. In this framework, we aim at determining an appropriate Sample Size so that the posterior expected loss incurred by Ee in taking the optimal action of Eo is sufficiently small. We develop general results for the one-parameter exponential family under quadratic loss and analyze the interactive impact of the prior beliefs of the three different parties on the resulting Sample Sizes. Relationships with other Sample Size Determination criteria are explored. Copyright © 2016 John Wiley & Sons, Ltd.
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robust bayesian Sample Size Determination in clinical trials
Statistics in Medicine, 2008Co-Authors: Pierpaolo Brutti, Fulvio De Santis, Stefania GubbiottiAbstract:This article deals with Determination of a Sample Size that guarantees the success of a trial. We follow a Bayesian approach and we say an experiment is successful if it yields a large posterior probability that an unknown parameter of interest (an unknown treatment effect or an effects-difference) is greater than a chosen threshold. In this context, a straightforward Sample Size criterion is to select the minimal number of observations so that the predictive probability of a successful trial is sufficiently large. In the paper we address the most typical criticism to Bayesian methods—their sensitivity to prior assumptions—by proposing a robust version of this Sample Size criterion. Specifically, instead of a single distribution, we consider a class of plausible priors for the parameter of interest. Robust Sample Sizes are then selected by looking at the predictive distribution of the lower bound of the posterior probability that the unknown parameter is greater than a chosen threshold. For their flexibility and mathematical tractability, we consider classes of e-contamination priors. As specific applications we consider Sample Size Determination for a Phase III trial. Copyright © 2008 John Wiley & Sons, Ltd.
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robust bayesian Sample Size Determination in clinical trials
Statistics in Medicine, 2008Co-Authors: Pierpaolo Brutti, Fulvio De Santis, Stefania GubbiottiAbstract:This article deals with Determination of a Sample Size that guarantees the success of a trial. We follow a Bayesian approach and we say an experiment is successful if it yields a large posterior probability that an unknown parameter of interest (an unknown treatment effect or an effects-difference) is greater than a chosen threshold. In this context, a straightforward Sample Size criterion is to select the minimal number of observations so that the predictive probability of a successful trial is sufficiently large. In the paper we address the most typical criticism to Bayesian methods-their sensitivity to prior assumptions-by proposing a robust version of this Sample Size criterion. Specifically, instead of a single distribution, we consider a class of plausible priors for the parameter of interest. Robust Sample Sizes are then selected by looking at the predictive distribution of the lower bound of the posterior probability that the unknown parameter is greater than a chosen threshold. For their flexibility and mathematical tractability, we consider classes of epsilon-contamination priors. As specific applications we consider Sample Size Determination for a Phase III trial.
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Bayesian Sample Size Determination and re-estimation using mixtures of prior distributions
2008Co-Authors: Pierpaolo Brutti, Stefania Gubbiotti, Fulvio De SantisAbstract:In this paper we propose a predictive Bayesian approach to Sample Size Determination and reestimation in clinical trials, in the presence of multiple sources of prior information. The method we suggest is based on the use of mixtures of prior distributions for the unknown quantity of interest, typically an unknown effect or an unknown effects-difference. Methodologies are developed using normal models with mixtures of conjugate priors. In particular we extend the Sample Size Determination analysis of [1] and the Sample Size re-estimation technique of [2].
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Robust Bayesian Sample Size Determination for Bernoulli trials
2007Co-Authors: Fulvio De Santis, Stefania GubbiottiAbstract:In a Bayesian framework a robust approach is proposed for Sample Size Determination (SSD) in a Bernoulli trial. The intent is to evaluate the impact of prior information, to define robust methods for choosing the Sample Size and to guarantee stable inferential conclusions. In this work we focus on the probability of success of a given treatment. First, the standard Bayesian method is presented. Historical information or experts’ beliefs are taken into account and formalized by an analysis prior distribution. Here we choose a Beta distribution, so that the derived posterior still belongs to the same family. A posterior quantity of interest is considered, such as the posterior expected value of the probability of success. Since, before performing the experiment, the expectation is random we need to define a predictive criterion for determining the optimal Sample Size. In this case the criterion is based on the predictive expected value, which is calculated with respect to the marginal distribution of the data. In order to avoid the sensitivity of the standard method to the prior specification, it is possible to introduce a robust approach, considering a class of priors instead of a single prior. Therefore, the focus is on the bounds of the posterior quantity of interest. Here, we choose the epsilon-contamination class, that guarantees both computational advantages and a straightforward interpretation. Two graphical tools are proposed to compare the standard SSD method with the robust one. The first consists in plotting the robust criterion with respect to the different levels of contamination, in correspondence to the standard optimal Sample Size. Alternatevely, both standard and robust criteria can be represented for increasing values of n, for a fixed level of contamination. An example is provided to show how the proposed robust approach brings to a more cautious choice, namely requires more individuals to be enrolled in the trial. The proposed approach may help the experimenter showing that standard methods are not always trustworthy and they should be used with caution. Sometimes if prior information and uncertainty on the parameter of interest are not properly accounted for, one may indeed choose inadequate Sample Sizes. References Berger, J. O. (1984). The Robust Bayesian Viewpoint (with discussion), in Robustness of Bayesian Analysis, ed. J. Kadane, Amsterdam: North-Holland,pp. 63–134. De Santis F. (2006). Sample Size Determination for robust Bayesian analysis, Journal of the American Statistical Association, Vol. 101, N. 473, 278-291. Greenhouse J.B., Wasserman L. (1995). Robust Bayesian methods for monitoring clinical trials, Statistics in Medicine, Vol. 14, 1379-1391. Sivaganesan S., Berger J.O. (1989). Ranges of posterior measures for priors with unimodal contaminations, The Annals of Statistics, Vol.17, N. 2, 868-889. Spiegelhalter D.J., Abrams K.R., Miles J.P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation, Statistics in Practice, Wiley. Wang F., Gelfand A.E. (2002). A simulation-based approach to Bayesian Sample Size Determination for performance under a given model and for separating models, Statistical Science, Vol 17, N. 2, 193-208.
Fulvio De Santis - One of the best experts on this subject based on the ideXlab platform.
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A decision‐theoretic approach to Sample Size Determination under several priors
Applied Stochastic Models in Business and Industry, 2016Co-Authors: Fulvio De Santis, Stefania GubbiottiAbstract:In this article, we consider Sample Size Determination for experiments in which estimation and design are performed by multiple parties. This problem has relevant applications in contexts involving adversarial decision makers, such as control theory, marketing, and drug testing. Specifically, we adopt a decision-theoretic perspective, and we assume that a decision on an unknown parameter of a statistical model involves two actors, Ee and Eo, who share the same data and loss function but not the same prior beliefs on the parameter. We also suppose that Ee has to use Eo's optimal action, and we finally assume that the experiment is planned by a third party, Pd. In this framework, we aim at determining an appropriate Sample Size so that the posterior expected loss incurred by Ee in taking the optimal action of Eo is sufficiently small. We develop general results for the one-parameter exponential family under quadratic loss and analyze the interactive impact of the prior beliefs of the three different parties on the resulting Sample Sizes. Relationships with other Sample Size Determination criteria are explored. Copyright © 2016 John Wiley & Sons, Ltd.
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robust bayesian Sample Size Determination in clinical trials
Statistics in Medicine, 2008Co-Authors: Pierpaolo Brutti, Fulvio De Santis, Stefania GubbiottiAbstract:This article deals with Determination of a Sample Size that guarantees the success of a trial. We follow a Bayesian approach and we say an experiment is successful if it yields a large posterior probability that an unknown parameter of interest (an unknown treatment effect or an effects-difference) is greater than a chosen threshold. In this context, a straightforward Sample Size criterion is to select the minimal number of observations so that the predictive probability of a successful trial is sufficiently large. In the paper we address the most typical criticism to Bayesian methods—their sensitivity to prior assumptions—by proposing a robust version of this Sample Size criterion. Specifically, instead of a single distribution, we consider a class of plausible priors for the parameter of interest. Robust Sample Sizes are then selected by looking at the predictive distribution of the lower bound of the posterior probability that the unknown parameter is greater than a chosen threshold. For their flexibility and mathematical tractability, we consider classes of e-contamination priors. As specific applications we consider Sample Size Determination for a Phase III trial. Copyright © 2008 John Wiley & Sons, Ltd.
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robust bayesian Sample Size Determination in clinical trials
Statistics in Medicine, 2008Co-Authors: Pierpaolo Brutti, Fulvio De Santis, Stefania GubbiottiAbstract:This article deals with Determination of a Sample Size that guarantees the success of a trial. We follow a Bayesian approach and we say an experiment is successful if it yields a large posterior probability that an unknown parameter of interest (an unknown treatment effect or an effects-difference) is greater than a chosen threshold. In this context, a straightforward Sample Size criterion is to select the minimal number of observations so that the predictive probability of a successful trial is sufficiently large. In the paper we address the most typical criticism to Bayesian methods-their sensitivity to prior assumptions-by proposing a robust version of this Sample Size criterion. Specifically, instead of a single distribution, we consider a class of plausible priors for the parameter of interest. Robust Sample Sizes are then selected by looking at the predictive distribution of the lower bound of the posterior probability that the unknown parameter is greater than a chosen threshold. For their flexibility and mathematical tractability, we consider classes of epsilon-contamination priors. As specific applications we consider Sample Size Determination for a Phase III trial.
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Bayesian Sample Size Determination and re-estimation using mixtures of prior distributions
2008Co-Authors: Pierpaolo Brutti, Stefania Gubbiotti, Fulvio De SantisAbstract:In this paper we propose a predictive Bayesian approach to Sample Size Determination and reestimation in clinical trials, in the presence of multiple sources of prior information. The method we suggest is based on the use of mixtures of prior distributions for the unknown quantity of interest, typically an unknown effect or an unknown effects-difference. Methodologies are developed using normal models with mixtures of conjugate priors. In particular we extend the Sample Size Determination analysis of [1] and the Sample Size re-estimation technique of [2].
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Robust Bayesian Sample Size Determination for Bernoulli trials
2007Co-Authors: Fulvio De Santis, Stefania GubbiottiAbstract:In a Bayesian framework a robust approach is proposed for Sample Size Determination (SSD) in a Bernoulli trial. The intent is to evaluate the impact of prior information, to define robust methods for choosing the Sample Size and to guarantee stable inferential conclusions. In this work we focus on the probability of success of a given treatment. First, the standard Bayesian method is presented. Historical information or experts’ beliefs are taken into account and formalized by an analysis prior distribution. Here we choose a Beta distribution, so that the derived posterior still belongs to the same family. A posterior quantity of interest is considered, such as the posterior expected value of the probability of success. Since, before performing the experiment, the expectation is random we need to define a predictive criterion for determining the optimal Sample Size. In this case the criterion is based on the predictive expected value, which is calculated with respect to the marginal distribution of the data. In order to avoid the sensitivity of the standard method to the prior specification, it is possible to introduce a robust approach, considering a class of priors instead of a single prior. Therefore, the focus is on the bounds of the posterior quantity of interest. Here, we choose the epsilon-contamination class, that guarantees both computational advantages and a straightforward interpretation. Two graphical tools are proposed to compare the standard SSD method with the robust one. The first consists in plotting the robust criterion with respect to the different levels of contamination, in correspondence to the standard optimal Sample Size. Alternatevely, both standard and robust criteria can be represented for increasing values of n, for a fixed level of contamination. An example is provided to show how the proposed robust approach brings to a more cautious choice, namely requires more individuals to be enrolled in the trial. The proposed approach may help the experimenter showing that standard methods are not always trustworthy and they should be used with caution. Sometimes if prior information and uncertainty on the parameter of interest are not properly accounted for, one may indeed choose inadequate Sample Sizes. References Berger, J. O. (1984). The Robust Bayesian Viewpoint (with discussion), in Robustness of Bayesian Analysis, ed. J. Kadane, Amsterdam: North-Holland,pp. 63–134. De Santis F. (2006). Sample Size Determination for robust Bayesian analysis, Journal of the American Statistical Association, Vol. 101, N. 473, 278-291. Greenhouse J.B., Wasserman L. (1995). Robust Bayesian methods for monitoring clinical trials, Statistics in Medicine, Vol. 14, 1379-1391. Sivaganesan S., Berger J.O. (1989). Ranges of posterior measures for priors with unimodal contaminations, The Annals of Statistics, Vol.17, N. 2, 868-889. Spiegelhalter D.J., Abrams K.R., Miles J.P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation, Statistics in Practice, Wiley. Wang F., Gelfand A.E. (2002). A simulation-based approach to Bayesian Sample Size Determination for performance under a given model and for separating models, Statistical Science, Vol 17, N. 2, 193-208.
Dean M Young - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Sample-Size Determination for two independent Poisson rates
Computer Methods and Programs in Biomedicine, 2010Co-Authors: Austin L. Hand, James D Stamey, Dean M YoungAbstract:Abstract: Because of the high cost and time constraints for clinical trials, researchers often need to determine the smallest Sample Size that provides accurate inferences for a parameter of interest. Although most experimenters have employed frequentist Sample-Size Determination methods, the Bayesian paradigm offers a wide variety of Sample-Size Determination methodologies. Bayesian Sample-Size Determination methods are becoming increasingly more popular in clinical trials because of their flexibility and easy interpretation inferences. Recently, Bayesian approaches have been used to determine the Sample Size of a single Poisson rate parameter in a clinical trial setting. In this paper, we extend these results to the comparison of two Poisson rates and develop methods for Sample-Size Determination for hypothesis testing in a Bayesian context. We have created functions in R to determine the parameters for the conjugate gamma prior and calculate the Sample Size for the average length criterion and average power methods. We also provide two examples that implement our Sample-Size Determination methods using clinical data.
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Bayesian Sample-Size Determination for one and two Poisson rate parameters with applications to quality control
Journal of Applied Statistics, 2006Co-Authors: James D Stamey, Dean M Young, Tom L. BratcherAbstract:We formulate Bayesian approaches to the problems of determining the required Sample Size for Bayesian interval estimators of a predetermined length for a single Poisson rate, for the difference between two Poisson rates, and for the ratio of two Poisson rates. We demonstrate the efficacy of our Bayesian-based Sample-Size Determination method with two real-data quality-control examples and compare the results to frequentist Sample-Size Determination methods.
Pierpaolo Brutti - One of the best experts on this subject based on the ideXlab platform.
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robust bayesian Sample Size Determination in clinical trials
Statistics in Medicine, 2008Co-Authors: Pierpaolo Brutti, Fulvio De Santis, Stefania GubbiottiAbstract:This article deals with Determination of a Sample Size that guarantees the success of a trial. We follow a Bayesian approach and we say an experiment is successful if it yields a large posterior probability that an unknown parameter of interest (an unknown treatment effect or an effects-difference) is greater than a chosen threshold. In this context, a straightforward Sample Size criterion is to select the minimal number of observations so that the predictive probability of a successful trial is sufficiently large. In the paper we address the most typical criticism to Bayesian methods—their sensitivity to prior assumptions—by proposing a robust version of this Sample Size criterion. Specifically, instead of a single distribution, we consider a class of plausible priors for the parameter of interest. Robust Sample Sizes are then selected by looking at the predictive distribution of the lower bound of the posterior probability that the unknown parameter is greater than a chosen threshold. For their flexibility and mathematical tractability, we consider classes of e-contamination priors. As specific applications we consider Sample Size Determination for a Phase III trial. Copyright © 2008 John Wiley & Sons, Ltd.
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robust bayesian Sample Size Determination in clinical trials
Statistics in Medicine, 2008Co-Authors: Pierpaolo Brutti, Fulvio De Santis, Stefania GubbiottiAbstract:This article deals with Determination of a Sample Size that guarantees the success of a trial. We follow a Bayesian approach and we say an experiment is successful if it yields a large posterior probability that an unknown parameter of interest (an unknown treatment effect or an effects-difference) is greater than a chosen threshold. In this context, a straightforward Sample Size criterion is to select the minimal number of observations so that the predictive probability of a successful trial is sufficiently large. In the paper we address the most typical criticism to Bayesian methods-their sensitivity to prior assumptions-by proposing a robust version of this Sample Size criterion. Specifically, instead of a single distribution, we consider a class of plausible priors for the parameter of interest. Robust Sample Sizes are then selected by looking at the predictive distribution of the lower bound of the posterior probability that the unknown parameter is greater than a chosen threshold. For their flexibility and mathematical tractability, we consider classes of epsilon-contamination priors. As specific applications we consider Sample Size Determination for a Phase III trial.
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Bayesian Sample Size Determination and re-estimation using mixtures of prior distributions
2008Co-Authors: Pierpaolo Brutti, Stefania Gubbiotti, Fulvio De SantisAbstract:In this paper we propose a predictive Bayesian approach to Sample Size Determination and reestimation in clinical trials, in the presence of multiple sources of prior information. The method we suggest is based on the use of mixtures of prior distributions for the unknown quantity of interest, typically an unknown effect or an unknown effects-difference. Methodologies are developed using normal models with mixtures of conjugate priors. In particular we extend the Sample Size Determination analysis of [1] and the Sample Size re-estimation technique of [2].
