The Experts below are selected from a list of 19197 Experts worldwide ranked by ideXlab platform
Van Der Schaar Mihaela - One of the best experts on this subject based on the ideXlab platform.
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SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups
2021Co-Authors: Lee Hyun-suk, Shen Cong, Zame William, Lee Jang-won, Van Der Schaar MihaelaAbstract:Phase I clinical trials are designed to test the safety (non-toxicity) of Drugs and find the maximum tolerated Dose (MTD). This task becomes significantly more challenging when Multiple-Drug Dose-combinations (DC) are involved, due to the inherent conflict between the exponentially increasing DC candidates and the limited patient budget. This paper proposes a novel Bayesian design, SDF-Bayes, for finding the MTD for Drug combinations in the presence of safety constraints. Rather than the conventional principle of escalating or de-escalating the current Dose of one Drug (perhaps alternating between Drugs), SDF-Bayes proceeds by cautious optimism: it chooses the next DC that, on the basis of current information, is most likely to be the MTD (optimism), subject to the constraint that it only chooses DCs that have a high probability of being safe (caution). We also propose an extension, SDF-Bayes-AR, that accounts for patient heterogeneity and enables heterogeneous patient recruitment. Extensive experiments based on both synthetic and real-world datasets demonstrate the advantages of SDF-Bayes over state of the art DC trial designs in terms of accuracy and safety.Comment: Accepted to AISTATS 202
Mihaela Van Der Schaar - One of the best experts on this subject based on the ideXlab platform.
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sdf bayes cautious optimism in safe Dose finding clinical trials with Drug combinations and heterogeneous patient groups
arXiv: Learning, 2021Co-Authors: Hyunsuk Lee, Cong Shen, William R Zame, Jangwon Lee, Mihaela Van Der SchaarAbstract:Phase I clinical trials are designed to test the safety (non-toxicity) of Drugs and find the maximum tolerated Dose (MTD). This task becomes significantly more challenging when Multiple-Drug Dose-combinations (DC) are involved, due to the inherent conflict between the exponentially increasing DC candidates and the limited patient budget. This paper proposes a novel Bayesian design, SDF-Bayes, for finding the MTD for Drug combinations in the presence of safety constraints. Rather than the conventional principle of escalating or de-escalating the current Dose of one Drug (perhaps alternating between Drugs), SDF-Bayes proceeds by cautious optimism: it chooses the next DC that, on the basis of current information, is most likely to be the MTD (optimism), subject to the constraint that it only chooses DCs that have a high probability of being safe (caution). We also propose an extension, SDF-Bayes-AR, that accounts for patient heterogeneity and enables heterogeneous patient recruitment. Extensive experiments based on both synthetic and real-world datasets demonstrate the advantages of SDF-Bayes over state of the art DC trial designs in terms of accuracy and safety.
Lee Hyun-suk - One of the best experts on this subject based on the ideXlab platform.
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SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups
2021Co-Authors: Lee Hyun-suk, Shen Cong, Zame William, Lee Jang-won, Van Der Schaar MihaelaAbstract:Phase I clinical trials are designed to test the safety (non-toxicity) of Drugs and find the maximum tolerated Dose (MTD). This task becomes significantly more challenging when Multiple-Drug Dose-combinations (DC) are involved, due to the inherent conflict between the exponentially increasing DC candidates and the limited patient budget. This paper proposes a novel Bayesian design, SDF-Bayes, for finding the MTD for Drug combinations in the presence of safety constraints. Rather than the conventional principle of escalating or de-escalating the current Dose of one Drug (perhaps alternating between Drugs), SDF-Bayes proceeds by cautious optimism: it chooses the next DC that, on the basis of current information, is most likely to be the MTD (optimism), subject to the constraint that it only chooses DCs that have a high probability of being safe (caution). We also propose an extension, SDF-Bayes-AR, that accounts for patient heterogeneity and enables heterogeneous patient recruitment. Extensive experiments based on both synthetic and real-world datasets demonstrate the advantages of SDF-Bayes over state of the art DC trial designs in terms of accuracy and safety.Comment: Accepted to AISTATS 202
Hyunsuk Lee - One of the best experts on this subject based on the ideXlab platform.
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sdf bayes cautious optimism in safe Dose finding clinical trials with Drug combinations and heterogeneous patient groups
arXiv: Learning, 2021Co-Authors: Hyunsuk Lee, Cong Shen, William R Zame, Jangwon Lee, Mihaela Van Der SchaarAbstract:Phase I clinical trials are designed to test the safety (non-toxicity) of Drugs and find the maximum tolerated Dose (MTD). This task becomes significantly more challenging when Multiple-Drug Dose-combinations (DC) are involved, due to the inherent conflict between the exponentially increasing DC candidates and the limited patient budget. This paper proposes a novel Bayesian design, SDF-Bayes, for finding the MTD for Drug combinations in the presence of safety constraints. Rather than the conventional principle of escalating or de-escalating the current Dose of one Drug (perhaps alternating between Drugs), SDF-Bayes proceeds by cautious optimism: it chooses the next DC that, on the basis of current information, is most likely to be the MTD (optimism), subject to the constraint that it only chooses DCs that have a high probability of being safe (caution). We also propose an extension, SDF-Bayes-AR, that accounts for patient heterogeneity and enables heterogeneous patient recruitment. Extensive experiments based on both synthetic and real-world datasets demonstrate the advantages of SDF-Bayes over state of the art DC trial designs in terms of accuracy and safety.
Shen Cong - One of the best experts on this subject based on the ideXlab platform.
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SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups
2021Co-Authors: Lee Hyun-suk, Shen Cong, Zame William, Lee Jang-won, Van Der Schaar MihaelaAbstract:Phase I clinical trials are designed to test the safety (non-toxicity) of Drugs and find the maximum tolerated Dose (MTD). This task becomes significantly more challenging when Multiple-Drug Dose-combinations (DC) are involved, due to the inherent conflict between the exponentially increasing DC candidates and the limited patient budget. This paper proposes a novel Bayesian design, SDF-Bayes, for finding the MTD for Drug combinations in the presence of safety constraints. Rather than the conventional principle of escalating or de-escalating the current Dose of one Drug (perhaps alternating between Drugs), SDF-Bayes proceeds by cautious optimism: it chooses the next DC that, on the basis of current information, is most likely to be the MTD (optimism), subject to the constraint that it only chooses DCs that have a high probability of being safe (caution). We also propose an extension, SDF-Bayes-AR, that accounts for patient heterogeneity and enables heterogeneous patient recruitment. Extensive experiments based on both synthetic and real-world datasets demonstrate the advantages of SDF-Bayes over state of the art DC trial designs in terms of accuracy and safety.Comment: Accepted to AISTATS 202
