The Experts below are selected from a list of 40569 Experts worldwide ranked by ideXlab platform
Cyrus R Mehta - One of the best experts on this subject based on the ideXlab platform.
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adaptive extensions of a two stage group Sequential Procedure for testing primary and secondary endpoints ii sample size re estimation
Statistics in Medicine, 2012Co-Authors: Ajit C Tamhane, Cyrus R MehtaAbstract:In this Part II of the paper on adaptive extensions of a two-stage group Sequential Procedure (GSP) proposed by Tamhane, Mehta and Liu [1] (referred to as TML hereafter) for testing a primary and a secondary endpoint, we focus on the second stage sample size re-estimation based on thefirst stage data. First we show that if we use the Cui, Huang and Wang [2] (referred to as CHW hereafter) statistics at the second stage then we can use the same primary and the secondary boundaries as for the original Procedure (without sample size re-estimation) and still control the type I familywise error rate (FWER). This extends their result for the single endpoint case. We further show that the secondary boundary can be sharpened in this case by taking the unknown correlation coefficient ρ between the primary and secondary endpoints into account through the use of the confidence limit method proposed in Part I of this paper [3]. If we use the sufficient statistics instead of the CHW statistics then we need to modify both the primary and secondary boundaries; otherwise the error rate can get inflated. We show how to modify the boundaries of the original GSP to control the FWER. Power comparisons between competing Procedures are provided. The Procedures are illustrated with a clinical trial example. Copyright c ⃝ 0000 John Wiley & Sons, Ltd.
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adaptive extensions of a two stage group Sequential Procedure for testing primary and secondary endpoints i unknown correlation between the endpoints
Statistics in Medicine, 2012Co-Authors: Ajit C Tamhane, Cyrus R MehtaAbstract:In a previous paper we studied a two-stage group Sequential Procedure (GSP) for testing primary and secondary endpoints where the primary endpoint serves as a gatekeeper for the secondary endpoint. We assumed a simple setup of a bivariate normal distribution for the two endpoints with the correlation coefficient ρ between them being either an unknown nuisance parameter or a known constant. Under the former assumption, we used the least favorable value of ρ = 1 to compute the critical boundaries of a conservative GSP. Under the latter assumption, we computed the critical boundaries of an exact GSP. However, neither assumption is very practical. The ρ = 1 assumption is too conservative resulting in loss of power, whereas the known ρ assumption is never true in practice. In this part I of a two-part paper on adaptive extensions of this two-stage Procedure (part II deals with sample size re-estimation), we propose an intermediate approach that uses the sample correlation coefficient r from the first-stage data to adaptively adjust the secondary boundary after accounting for the sampling error in r via an upper confidence limit on ρ by using a method due to Berger and Boos. We show via simulation that this approach achieves 5–11% absolute secondary power gain for ρ ≤0.5. The preferred boundary combination in terms of high primary as well as secondary power is that of O'Brien and Fleming for the primary and of Pocock for the secondary. The proposed approach using this boundary combination achieves 72–84% relative secondary power gain (with respect to the exact GSP that assumes known ρ). We give a clinical trial example to illustrate the proposed Procedure. Copyright © 2012 John Wiley & Sons, Ltd.
James R. Wilson - One of the best experts on this subject based on the ideXlab platform.
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sequest a Sequential Procedure for estimating quantiles in steady state simulations
Operations Research, 2019Co-Authors: Christos Alexopoulos, David Goldsman, Anup Mokashi, Kai-wen Tien, James R. WilsonAbstract:Sequest is an automated Sequential simulation-analysis Procedure designed to provide improved point and confidence-interval (CI) estimators for a designated steady-state quantile by the use of batc...
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SPSTS: A Sequential Procedure for estimating the steady-state mean using standardized time series
IIE Transactions, 2016Co-Authors: Christos Alexopoulos, David Goldsman, Peng Tang, James R. WilsonAbstract:ABSTRACTThis article presents SPSTS, an automated Sequential Procedure for computing point and Confidence-Interval (CI) estimators for the steady-state mean of a simulation-generated process subject to user-specified requirements for the CI coverage probability and relative half-length. SPSTS is the first Sequential method based on Standardized Time Series (STS) area estimators of the steady-state variance parameter (i.e., the sum of covariances at all lags). Whereas its leading competitors rely on the method of batch means to remove bias due to the initial transient, estimate the variance parameter, and compute the CI, SPSTS relies on the signed areas corresponding to two orthonormal STS area variance estimators for these tasks. In successive stages of SPSTS, standard tests for normality and independence are applied to the signed areas to determine (i) the length of the warm-up period, and (ii) a batch size sufficient to ensure adequate convergence of the associated STS area variance estimators to their ...
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Sequest: A Sequential Procedure for estimating steady-state quantiles
Proceedings of the Winter Simulation Conference 2014, 2014Co-Authors: Christos Alexopoulos, David Goldsman, Anup Mokashi, Kai-wen Tien, James R. WilsonAbstract:Sequest is a fully Sequential Procedure that delivers improved point and confidence-interval (CI) estimators for a designated steady-state quantile by exploiting a combination of ideas from batching and sectioning. Sequest incorporates effective methods to do the following: (a) eliminate bias in the sectioning-based point estimator that is caused by initialization of the simulation or an inadequate simulation run length (sample size); and (b) adjust the CI half-length for the effects of skewness or correlation in the batching-based point estimators of the designated quantile. Sequest delivers a CI designed to satisfy user-specified requirements concerning both the CI's coverage probability and its absolute or relative precision. We found that Sequest exhibited good small- and large-sample properties in a preliminary evaluation of the Procedure's performance on a suite of test problems that includes some problems designed to “stress test” the Procedure.
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Winter Simulation Conference - Sequest: a Sequential Procedure for estimating steady-state quantiles
Proceedings of the Winter Simulation Conference 2014, 2014Co-Authors: Christos Alexopoulos, David Goldsman, Anup Mokashi, Kai-wen Tien, Rong Nie, Qing Sun, James R. WilsonAbstract:Sequest is a fully Sequential Procedure that delivers improved point and confidence-interval (CI) estimators for a designated steady-state quantile by exploiting a combination of ideas from batching and sectioning. Sequest incorporates effective methods to do the following: (a) eliminate bias in the sectioning-based point estimator that is caused by initialization of the simulation or an inadequate simulation run length (sample size); and (b) adjust the CI half-length for the effects of skewness or correlation in the batching-based point estimators of the designated quantile. Sequest delivers a CI designed to satisfy user-specified requirements concerning both the CI's coverage probability and its absolute or relative precision. We found that Sequest exhibited good small- and large-sample properties in a preliminary evaluation of the Procedure's performance on a suite of test problems that includes some problems designed to "stress test" the Procedure.
Christos Alexopoulos - One of the best experts on this subject based on the ideXlab platform.
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sequest a Sequential Procedure for estimating quantiles in steady state simulations
Operations Research, 2019Co-Authors: Christos Alexopoulos, David Goldsman, Anup Mokashi, Kai-wen Tien, James R. WilsonAbstract:Sequest is an automated Sequential simulation-analysis Procedure designed to provide improved point and confidence-interval (CI) estimators for a designated steady-state quantile by the use of batc...
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SPSTS: A Sequential Procedure for estimating the steady-state mean using standardized time series
IIE Transactions, 2016Co-Authors: Christos Alexopoulos, David Goldsman, Peng Tang, James R. WilsonAbstract:ABSTRACTThis article presents SPSTS, an automated Sequential Procedure for computing point and Confidence-Interval (CI) estimators for the steady-state mean of a simulation-generated process subject to user-specified requirements for the CI coverage probability and relative half-length. SPSTS is the first Sequential method based on Standardized Time Series (STS) area estimators of the steady-state variance parameter (i.e., the sum of covariances at all lags). Whereas its leading competitors rely on the method of batch means to remove bias due to the initial transient, estimate the variance parameter, and compute the CI, SPSTS relies on the signed areas corresponding to two orthonormal STS area variance estimators for these tasks. In successive stages of SPSTS, standard tests for normality and independence are applied to the signed areas to determine (i) the length of the warm-up period, and (ii) a batch size sufficient to ensure adequate convergence of the associated STS area variance estimators to their ...
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Sequest: A Sequential Procedure for estimating steady-state quantiles
Proceedings of the Winter Simulation Conference 2014, 2014Co-Authors: Christos Alexopoulos, David Goldsman, Anup Mokashi, Kai-wen Tien, James R. WilsonAbstract:Sequest is a fully Sequential Procedure that delivers improved point and confidence-interval (CI) estimators for a designated steady-state quantile by exploiting a combination of ideas from batching and sectioning. Sequest incorporates effective methods to do the following: (a) eliminate bias in the sectioning-based point estimator that is caused by initialization of the simulation or an inadequate simulation run length (sample size); and (b) adjust the CI half-length for the effects of skewness or correlation in the batching-based point estimators of the designated quantile. Sequest delivers a CI designed to satisfy user-specified requirements concerning both the CI's coverage probability and its absolute or relative precision. We found that Sequest exhibited good small- and large-sample properties in a preliminary evaluation of the Procedure's performance on a suite of test problems that includes some problems designed to “stress test” the Procedure.
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Winter Simulation Conference - Sequest: a Sequential Procedure for estimating steady-state quantiles
Proceedings of the Winter Simulation Conference 2014, 2014Co-Authors: Christos Alexopoulos, David Goldsman, Anup Mokashi, Kai-wen Tien, Rong Nie, Qing Sun, James R. WilsonAbstract:Sequest is a fully Sequential Procedure that delivers improved point and confidence-interval (CI) estimators for a designated steady-state quantile by exploiting a combination of ideas from batching and sectioning. Sequest incorporates effective methods to do the following: (a) eliminate bias in the sectioning-based point estimator that is caused by initialization of the simulation or an inadequate simulation run length (sample size); and (b) adjust the CI half-length for the effects of skewness or correlation in the batching-based point estimators of the designated quantile. Sequest delivers a CI designed to satisfy user-specified requirements concerning both the CI's coverage probability and its absolute or relative precision. We found that Sequest exhibited good small- and large-sample properties in a preliminary evaluation of the Procedure's performance on a suite of test problems that includes some problems designed to "stress test" the Procedure.
Ajit C Tamhane - One of the best experts on this subject based on the ideXlab platform.
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adaptive extensions of a two stage group Sequential Procedure for testing primary and secondary endpoints ii sample size re estimation
Statistics in Medicine, 2012Co-Authors: Ajit C Tamhane, Cyrus R MehtaAbstract:In this Part II of the paper on adaptive extensions of a two-stage group Sequential Procedure (GSP) proposed by Tamhane, Mehta and Liu [1] (referred to as TML hereafter) for testing a primary and a secondary endpoint, we focus on the second stage sample size re-estimation based on thefirst stage data. First we show that if we use the Cui, Huang and Wang [2] (referred to as CHW hereafter) statistics at the second stage then we can use the same primary and the secondary boundaries as for the original Procedure (without sample size re-estimation) and still control the type I familywise error rate (FWER). This extends their result for the single endpoint case. We further show that the secondary boundary can be sharpened in this case by taking the unknown correlation coefficient ρ between the primary and secondary endpoints into account through the use of the confidence limit method proposed in Part I of this paper [3]. If we use the sufficient statistics instead of the CHW statistics then we need to modify both the primary and secondary boundaries; otherwise the error rate can get inflated. We show how to modify the boundaries of the original GSP to control the FWER. Power comparisons between competing Procedures are provided. The Procedures are illustrated with a clinical trial example. Copyright c ⃝ 0000 John Wiley & Sons, Ltd.
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adaptive extensions of a two stage group Sequential Procedure for testing primary and secondary endpoints i unknown correlation between the endpoints
Statistics in Medicine, 2012Co-Authors: Ajit C Tamhane, Cyrus R MehtaAbstract:In a previous paper we studied a two-stage group Sequential Procedure (GSP) for testing primary and secondary endpoints where the primary endpoint serves as a gatekeeper for the secondary endpoint. We assumed a simple setup of a bivariate normal distribution for the two endpoints with the correlation coefficient ρ between them being either an unknown nuisance parameter or a known constant. Under the former assumption, we used the least favorable value of ρ = 1 to compute the critical boundaries of a conservative GSP. Under the latter assumption, we computed the critical boundaries of an exact GSP. However, neither assumption is very practical. The ρ = 1 assumption is too conservative resulting in loss of power, whereas the known ρ assumption is never true in practice. In this part I of a two-part paper on adaptive extensions of this two-stage Procedure (part II deals with sample size re-estimation), we propose an intermediate approach that uses the sample correlation coefficient r from the first-stage data to adaptively adjust the secondary boundary after accounting for the sampling error in r via an upper confidence limit on ρ by using a method due to Berger and Boos. We show via simulation that this approach achieves 5–11% absolute secondary power gain for ρ ≤0.5. The preferred boundary combination in terms of high primary as well as secondary power is that of O'Brien and Fleming for the primary and of Pocock for the secondary. The proposed approach using this boundary combination achieves 72–84% relative secondary power gain (with respect to the exact GSP that assumes known ρ). We give a clinical trial example to illustrate the proposed Procedure. Copyright © 2012 John Wiley & Sons, Ltd.
David Goldsman - One of the best experts on this subject based on the ideXlab platform.
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sequest a Sequential Procedure for estimating quantiles in steady state simulations
Operations Research, 2019Co-Authors: Christos Alexopoulos, David Goldsman, Anup Mokashi, Kai-wen Tien, James R. WilsonAbstract:Sequest is an automated Sequential simulation-analysis Procedure designed to provide improved point and confidence-interval (CI) estimators for a designated steady-state quantile by the use of batc...
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SPSTS: A Sequential Procedure for estimating the steady-state mean using standardized time series
IIE Transactions, 2016Co-Authors: Christos Alexopoulos, David Goldsman, Peng Tang, James R. WilsonAbstract:ABSTRACTThis article presents SPSTS, an automated Sequential Procedure for computing point and Confidence-Interval (CI) estimators for the steady-state mean of a simulation-generated process subject to user-specified requirements for the CI coverage probability and relative half-length. SPSTS is the first Sequential method based on Standardized Time Series (STS) area estimators of the steady-state variance parameter (i.e., the sum of covariances at all lags). Whereas its leading competitors rely on the method of batch means to remove bias due to the initial transient, estimate the variance parameter, and compute the CI, SPSTS relies on the signed areas corresponding to two orthonormal STS area variance estimators for these tasks. In successive stages of SPSTS, standard tests for normality and independence are applied to the signed areas to determine (i) the length of the warm-up period, and (ii) a batch size sufficient to ensure adequate convergence of the associated STS area variance estimators to their ...
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Sequest: A Sequential Procedure for estimating steady-state quantiles
Proceedings of the Winter Simulation Conference 2014, 2014Co-Authors: Christos Alexopoulos, David Goldsman, Anup Mokashi, Kai-wen Tien, James R. WilsonAbstract:Sequest is a fully Sequential Procedure that delivers improved point and confidence-interval (CI) estimators for a designated steady-state quantile by exploiting a combination of ideas from batching and sectioning. Sequest incorporates effective methods to do the following: (a) eliminate bias in the sectioning-based point estimator that is caused by initialization of the simulation or an inadequate simulation run length (sample size); and (b) adjust the CI half-length for the effects of skewness or correlation in the batching-based point estimators of the designated quantile. Sequest delivers a CI designed to satisfy user-specified requirements concerning both the CI's coverage probability and its absolute or relative precision. We found that Sequest exhibited good small- and large-sample properties in a preliminary evaluation of the Procedure's performance on a suite of test problems that includes some problems designed to “stress test” the Procedure.
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Winter Simulation Conference - Sequest: a Sequential Procedure for estimating steady-state quantiles
Proceedings of the Winter Simulation Conference 2014, 2014Co-Authors: Christos Alexopoulos, David Goldsman, Anup Mokashi, Kai-wen Tien, Rong Nie, Qing Sun, James R. WilsonAbstract:Sequest is a fully Sequential Procedure that delivers improved point and confidence-interval (CI) estimators for a designated steady-state quantile by exploiting a combination of ideas from batching and sectioning. Sequest incorporates effective methods to do the following: (a) eliminate bias in the sectioning-based point estimator that is caused by initialization of the simulation or an inadequate simulation run length (sample size); and (b) adjust the CI half-length for the effects of skewness or correlation in the batching-based point estimators of the designated quantile. Sequest delivers a CI designed to satisfy user-specified requirements concerning both the CI's coverage probability and its absolute or relative precision. We found that Sequest exhibited good small- and large-sample properties in a preliminary evaluation of the Procedure's performance on a suite of test problems that includes some problems designed to "stress test" the Procedure.
