The Experts below are selected from a list of 38814 Experts worldwide ranked by ideXlab platform
Aki Tsuchiya - One of the best experts on this subject based on the ideXlab platform.
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using discrete choice experiments with duration to model eq 5d 5l health state preferences testing experimental design strategies
Medical Decision Making, 2017Co-Authors: Brendan Mulhern, Nick Bansback, Arne Risa Hole, Aki TsuchiyaAbstract:Background: Discrete choice experiments incorporating duration can be used to derive health state values for EQ-5D-5L. Yet, methodological issues relating to the duration attribute and the optimal way to select health states remain. The aims of this study were to: test increasing the number of duration levels and choice sets where duration varies (aim 1); compare designs with zero and non-zero prior values (aim 2); and investigate a novel, two-stage design to incorporate prior values (aim 3). Methods: Informed by zero and non-zero prior values, two efficient designs were developed, each consisting of 120 EQ-5D-5L health profile pairs with one of six duration levels (aims 1 and 2). Another 120 health state pairs were selected, with one of six duration levels allocated in a second stage based on existing Estimated Utility of the states (aim 3). An online sample of 2,002 members of the UK general population completed 10 choice sets each. Differences across the regression coefficients from the three designs w...
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using discrete choice experiments with duration to model eq 5d 5l health state preferences testing experimental design strategies
Medical Decision Making, 2017Co-Authors: Brendan Mulhern, Nick Bansback, Arne Risa Hole, Aki TsuchiyaAbstract:Background: Discrete choice experiments incorporating duration can be used to derive health state values for EQ-5D-5L. Yet, methodological issues relating to the duration attribute and the optimal way to select health states remain. The aims of this study were to: test increasing the number of duration levels and choice sets where duration varies (aim 1); compare designs with zero and non-zero prior values (aim 2); and investigate a novel, two-stage design to incorporate prior values (aim 3). Methods: Informed by zero and non-zero prior values, two efficient designs were developed, each consisting of 120 EQ-5D-5L health profile pairs with one of six duration levels (aims 1 and 2). Another 120 health state pairs were selected, with one of six duration levels allocated in a second stage based on existing Estimated Utility of the states (aim 3). An online sample of 2,002 members of the UK general population completed 10 choice sets each. Differences across the regression coefficients from the three designs were assessed. Results: The zero prior value design produced a model with coefficients that were generally logically ordered, but the non-zero prior value design resulted in a set of less ordered coefficients where some differed significantly. The two-stage design resulted in ordered and significant coefficients. The non-zero prior value design may include more “difficult” choice sets, based on the proportions choosing each profile. Conclusions: There is some indication of compromised “respondent efficiency”, suggesting that the use of non-zero prior values will not necessarily result in better overall precision. It is feasible to design discrete choice experiments in two stages by allocating duration values to EQ-5D-5L health state pairs based on estimates from prior studies.
Brendan Mulhern - One of the best experts on this subject based on the ideXlab platform.
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using discrete choice experiments with duration to model eq 5d 5l health state preferences testing experimental design strategies
Medical Decision Making, 2017Co-Authors: Brendan Mulhern, Nick Bansback, Arne Risa Hole, Aki TsuchiyaAbstract:Background: Discrete choice experiments incorporating duration can be used to derive health state values for EQ-5D-5L. Yet, methodological issues relating to the duration attribute and the optimal way to select health states remain. The aims of this study were to: test increasing the number of duration levels and choice sets where duration varies (aim 1); compare designs with zero and non-zero prior values (aim 2); and investigate a novel, two-stage design to incorporate prior values (aim 3). Methods: Informed by zero and non-zero prior values, two efficient designs were developed, each consisting of 120 EQ-5D-5L health profile pairs with one of six duration levels (aims 1 and 2). Another 120 health state pairs were selected, with one of six duration levels allocated in a second stage based on existing Estimated Utility of the states (aim 3). An online sample of 2,002 members of the UK general population completed 10 choice sets each. Differences across the regression coefficients from the three designs w...
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using discrete choice experiments with duration to model eq 5d 5l health state preferences testing experimental design strategies
Medical Decision Making, 2017Co-Authors: Brendan Mulhern, Nick Bansback, Arne Risa Hole, Aki TsuchiyaAbstract:Background: Discrete choice experiments incorporating duration can be used to derive health state values for EQ-5D-5L. Yet, methodological issues relating to the duration attribute and the optimal way to select health states remain. The aims of this study were to: test increasing the number of duration levels and choice sets where duration varies (aim 1); compare designs with zero and non-zero prior values (aim 2); and investigate a novel, two-stage design to incorporate prior values (aim 3). Methods: Informed by zero and non-zero prior values, two efficient designs were developed, each consisting of 120 EQ-5D-5L health profile pairs with one of six duration levels (aims 1 and 2). Another 120 health state pairs were selected, with one of six duration levels allocated in a second stage based on existing Estimated Utility of the states (aim 3). An online sample of 2,002 members of the UK general population completed 10 choice sets each. Differences across the regression coefficients from the three designs were assessed. Results: The zero prior value design produced a model with coefficients that were generally logically ordered, but the non-zero prior value design resulted in a set of less ordered coefficients where some differed significantly. The two-stage design resulted in ordered and significant coefficients. The non-zero prior value design may include more “difficult” choice sets, based on the proportions choosing each profile. Conclusions: There is some indication of compromised “respondent efficiency”, suggesting that the use of non-zero prior values will not necessarily result in better overall precision. It is feasible to design discrete choice experiments in two stages by allocating duration values to EQ-5D-5L health state pairs based on estimates from prior studies.
Nick Bansback - One of the best experts on this subject based on the ideXlab platform.
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using discrete choice experiments with duration to model eq 5d 5l health state preferences testing experimental design strategies
Medical Decision Making, 2017Co-Authors: Brendan Mulhern, Nick Bansback, Arne Risa Hole, Aki TsuchiyaAbstract:Background: Discrete choice experiments incorporating duration can be used to derive health state values for EQ-5D-5L. Yet, methodological issues relating to the duration attribute and the optimal way to select health states remain. The aims of this study were to: test increasing the number of duration levels and choice sets where duration varies (aim 1); compare designs with zero and non-zero prior values (aim 2); and investigate a novel, two-stage design to incorporate prior values (aim 3). Methods: Informed by zero and non-zero prior values, two efficient designs were developed, each consisting of 120 EQ-5D-5L health profile pairs with one of six duration levels (aims 1 and 2). Another 120 health state pairs were selected, with one of six duration levels allocated in a second stage based on existing Estimated Utility of the states (aim 3). An online sample of 2,002 members of the UK general population completed 10 choice sets each. Differences across the regression coefficients from the three designs w...
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using discrete choice experiments with duration to model eq 5d 5l health state preferences testing experimental design strategies
Medical Decision Making, 2017Co-Authors: Brendan Mulhern, Nick Bansback, Arne Risa Hole, Aki TsuchiyaAbstract:Background: Discrete choice experiments incorporating duration can be used to derive health state values for EQ-5D-5L. Yet, methodological issues relating to the duration attribute and the optimal way to select health states remain. The aims of this study were to: test increasing the number of duration levels and choice sets where duration varies (aim 1); compare designs with zero and non-zero prior values (aim 2); and investigate a novel, two-stage design to incorporate prior values (aim 3). Methods: Informed by zero and non-zero prior values, two efficient designs were developed, each consisting of 120 EQ-5D-5L health profile pairs with one of six duration levels (aims 1 and 2). Another 120 health state pairs were selected, with one of six duration levels allocated in a second stage based on existing Estimated Utility of the states (aim 3). An online sample of 2,002 members of the UK general population completed 10 choice sets each. Differences across the regression coefficients from the three designs were assessed. Results: The zero prior value design produced a model with coefficients that were generally logically ordered, but the non-zero prior value design resulted in a set of less ordered coefficients where some differed significantly. The two-stage design resulted in ordered and significant coefficients. The non-zero prior value design may include more “difficult” choice sets, based on the proportions choosing each profile. Conclusions: There is some indication of compromised “respondent efficiency”, suggesting that the use of non-zero prior values will not necessarily result in better overall precision. It is feasible to design discrete choice experiments in two stages by allocating duration values to EQ-5D-5L health state pairs based on estimates from prior studies.
Arne Risa Hole - One of the best experts on this subject based on the ideXlab platform.
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using discrete choice experiments with duration to model eq 5d 5l health state preferences testing experimental design strategies
Medical Decision Making, 2017Co-Authors: Brendan Mulhern, Nick Bansback, Arne Risa Hole, Aki TsuchiyaAbstract:Background: Discrete choice experiments incorporating duration can be used to derive health state values for EQ-5D-5L. Yet, methodological issues relating to the duration attribute and the optimal way to select health states remain. The aims of this study were to: test increasing the number of duration levels and choice sets where duration varies (aim 1); compare designs with zero and non-zero prior values (aim 2); and investigate a novel, two-stage design to incorporate prior values (aim 3). Methods: Informed by zero and non-zero prior values, two efficient designs were developed, each consisting of 120 EQ-5D-5L health profile pairs with one of six duration levels (aims 1 and 2). Another 120 health state pairs were selected, with one of six duration levels allocated in a second stage based on existing Estimated Utility of the states (aim 3). An online sample of 2,002 members of the UK general population completed 10 choice sets each. Differences across the regression coefficients from the three designs w...
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using discrete choice experiments with duration to model eq 5d 5l health state preferences testing experimental design strategies
Medical Decision Making, 2017Co-Authors: Brendan Mulhern, Nick Bansback, Arne Risa Hole, Aki TsuchiyaAbstract:Background: Discrete choice experiments incorporating duration can be used to derive health state values for EQ-5D-5L. Yet, methodological issues relating to the duration attribute and the optimal way to select health states remain. The aims of this study were to: test increasing the number of duration levels and choice sets where duration varies (aim 1); compare designs with zero and non-zero prior values (aim 2); and investigate a novel, two-stage design to incorporate prior values (aim 3). Methods: Informed by zero and non-zero prior values, two efficient designs were developed, each consisting of 120 EQ-5D-5L health profile pairs with one of six duration levels (aims 1 and 2). Another 120 health state pairs were selected, with one of six duration levels allocated in a second stage based on existing Estimated Utility of the states (aim 3). An online sample of 2,002 members of the UK general population completed 10 choice sets each. Differences across the regression coefficients from the three designs were assessed. Results: The zero prior value design produced a model with coefficients that were generally logically ordered, but the non-zero prior value design resulted in a set of less ordered coefficients where some differed significantly. The two-stage design resulted in ordered and significant coefficients. The non-zero prior value design may include more “difficult” choice sets, based on the proportions choosing each profile. Conclusions: There is some indication of compromised “respondent efficiency”, suggesting that the use of non-zero prior values will not necessarily result in better overall precision. It is feasible to design discrete choice experiments in two stages by allocating duration values to EQ-5D-5L health state pairs based on estimates from prior studies.
Costas J Spanos - One of the best experts on this subject based on the ideXlab platform.
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A Robust Utility Learning Framework via Inverse Optimization
IEEE Transactions on Control Systems Technology, 2018Co-Authors: Ioannis C Konstantakopoulos, Ming Jin, Lillian J. Ratliff, Shankar S. Sastry, Costas J SpanosAbstract:In many smart infrastructure applications, flexibility in achieving sustainability goals can be gained by engaging end users. However, these users often have heterogeneous preferences that are unknown to the decision maker tasked with improving operational efficiency. Modeling user interaction as a continuous game between noncooperative players, we propose a robust parametric Utility learning framework that employs constrained feasible generalized least squares estimation with heteroskedastic inference. To improve forecasting performance, we extend the robust Utility learning scheme by employing bootstrapping with bagging, bumping, and gradient boosting ensemble methods. Moreover, we estimate the noise covariance, which provides approximated correlations between players, which we leverage to develop a novel correlated Utility learning framework. We apply the proposed methods both to a toy example arising from Bertrand-Nash competition between two firms and to data from a social game experiment designed to encourage energy efficient behavior among smart building occupants. Using occupant voting data for shared resources such as lighting, we simulate the game defined by the Estimated Utility functions to demonstrate the performance of the proposed methods.
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a robust Utility learning framework via inverse optimization
arXiv: Computer Science and Game Theory, 2017Co-Authors: Ioannis C Konstantakopoulos, Ming Jin, Lillian J. Ratliff, Shankar S. Sastry, Costas J SpanosAbstract:In many smart infrastructure applications flexibility in achieving sustainability goals can be gained by engaging end-users. However, these users often have heterogeneous preferences that are unknown to the decision-maker tasked with improving operational efficiency. Modeling user interaction as a continuous game between non-cooperative players, we propose a robust parametric Utility learning framework that employs constrained feasible generalized least squares estimation with heteroskedastic inference. To improve forecasting performance, we extend the robust Utility learning scheme by employing bootstrapping with bagging, bumping, and gradient boosting ensemble methods. Moreover, we estimate the noise covariance which provides approximated correlations between players which we leverage to develop a novel correlated Utility learning framework. We apply the proposed methods both to a toy example arising from Bertrand-Nash competition between two firms as well as to data from a social game experiment designed to encourage energy efficient behavior amongst smart building occupants. Using occupant voting data for shared resources such as lighting, we simulate the game defined by the Estimated Utility functions to demonstrate the performance of the proposed methods.
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smart building energy efficiency via social game a robust Utility learning framework for closing the loop
Science of Smart City Operations and Platforms Engineering (SCOPE) in partnership with Global City Teams Challenge (GCTC) (SCOPE - GCTC) 2016 1st Inte, 2016Co-Authors: Ioannis C Konstantakopoulos, Ming Jin, Costas J Spanos, Lillian J. Ratliff, Shankar SastryAbstract:Given a non-cooperative, continuous game, we describe a framework for parametric Utility learning. Using heteroskedasticity inference, we adapt a Constrained Feasible Generalized Least Squares (cFGLS) Utility learning method in which estimator variance is reduced, unbiased, and consistent. We extend our Utility learning method using bootstrapping and bagging. We show the performance of the proposed method using data from a social game experiment designed to encourage energy efficient behavior amongst building occupants. Using occupant voting data we simulate the game defined by the Estimated Utility functions and show that the performance of our robust Utility learning method and quantify its improvement over classical methods such as Ordinary Least Squares (OLS).
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social game for building energy efficiency Utility learning simulation and analysis
arXiv: Optimization and Control, 2014Co-Authors: Ioannis C Konstantakopoulos, Ming Jin, Shankar Sastry, Lillian J. Ratliff, Costas J SpanosAbstract:We describe a social game that we designed for encouraging energy efficient behavior amongst building occupants with the aim of reducing overall energy consumption in the building. Occupants vote for their desired lighting level and win points which are used in a lottery based on how far their vote is from the maximum setting. We assume that the occupants are Utility maximizers and that their Utility functions capture the tradeoff between winning points and their comfort level. We model the occupants as non-cooperative agents in a continuous game and we characterize their play using the Nash equilibrium concept. Using occupant voting data, we parameterize their Utility functions and use a convex optimization problem to estimate the parameters. We simulate the game defined by the Estimated Utility functions and show that the Estimated model for occupant behavior is a good predictor of their actual behavior. In addition, we show that due to the social game, there is a significant reduction in energy consumption.