The Experts below are selected from a list of 67779 Experts worldwide ranked by ideXlab platform
Suejane Wang - One of the best experts on this subject based on the ideXlab platform.
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modified toxicity probability interval design a safer and more reliable method than the 3 3 design for practical phase i trials
Journal of Clinical Oncology, 2013Co-Authors: Yuan Ji, Suejane WangAbstract:The 3!3 design is the most common choice among clinicians for phase I dose-Escalation oncology trials. In recent reviews, more than 95% of phase I trials have been based on the 3!3 design. Given that it is intuitive and its implementation does not require a computer program, clinicians can conduct 3!3 dose Escalations in practice with virtually no logistic cost, and trial protocols based on the 3!3 design pass institutional review board and biostatistics reviews quickly. However, the performance of the 3!3 design has rarely been compared with model-based designs in simulation studies with matched sample sizes. In the vast majority of statistical literature, the 3!3 design has been shown to be inferior in identifying true maximum-tolerated doses (MTDs), although the sample size required by the 3!3 design is often orders-of-magnitude smaller than model-based designs. In this article, through comparative simulation studies with matched sample sizes, we demonstrate that the 3!3 design has higher risks of exposing patients to toxic doses above the MTD than the modified toxicity probability interval (mTPI) design, a newly developed adaptive method. In addition, compared with the mTPI design, the 3!3 design does not yield higher probabilities in identifying the correct MTD, even when the sample size is matched. Given that the mTPI design is equally transparent, costless to implement with free software, and more flexible in practical situations, we highly encourage its adoption in early dose-Escalation studies whenever the 3!3 design is also considered. We provide free software to allow direct comparisons of the 3!3 design with other model-based designs in simulation studies with matched sample sizes. J Clin Oncol 31:1785-1791. © 2013 by American Society of Clinical Oncology
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modified toxicity probability interval design a safer and more reliable method than the 3 3 design for practical phase i trials
Journal of Clinical Oncology, 2013Co-Authors: Suejane WangAbstract:The 3 + 3 design is the most common choice among clinicians for phase I dose-Escalation oncology trials. In recent reviews, more than 95% of phase I trials have been based on the 3 + 3 design. Given that it is intuitive and its implementation does not require a computer program, clinicians can conduct 3 + 3 dose Escalations in practice with virtually no logistic cost, and trial protocols based on the 3 + 3 design pass institutional review board and biostatistics reviews quickly. However, the performance of the 3 + 3 design has rarely been compared with model-based designs in simulation studies with matched sample sizes. In the vast majority of statistical literature, the 3 + 3 design has been shown to be inferior in identifying true maximum-tolerated doses (MTDs), although the sample size required by the 3 + 3 design is often orders-of-magnitude smaller than model-based designs. In this article, through comparative simulation studies with matched sample sizes, we demonstrate that the 3 + 3 design has higher risks of exposing patients to toxic doses above the MTD than the modified toxicity probability interval (mTPI) design, a newly developed adaptive method. In addition, compared with the mTPI design, the 3 + 3 design does not yield higher probabilities in identifying the correct MTD, even when the sample size is matched. Given that the mTPI design is equally transparent, costless to implement with free software, and more flexible in practical situations, we highly encourage its adoption in early dose-Escalation studies whenever the 3 + 3 design is also considered. We provide free software to allow direct comparisons of the 3 + 3 design with other model-based designs in simulation studies with matched sample sizes.
SØren L. Buhl - One of the best experts on this subject based on the ideXlab platform.
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what causes cost overrun in transport infrastructure projects
arXiv: General Finance, 2013Co-Authors: Bent Flyvbjerg, Mette Skamris K Holm, SØren L. BuhlAbstract:This article presents results from the first statistically significant study of causes of cost Escalation in transport infrastructure projects. The study is based on a sample of 258 rail, bridge, tunnel and road projects worth US$90 billion. The focus is on the dependence of cost Escalation on (1) length of project implementation phase, (2) size of project and (3) type of project ownership. First, it is found with very high statistical significance that cost Escalation is strongly dependent on length of implementation phase. The policy implications are clear: Decision makers and planners should be highly concerned about delays and long implementation phases because they translate into risks of substantial cost Escalations. Second, it is found that projects have grown larger over time and that for bridges and tunnels larger projects have larger percentage cost Escalations. Finally, by comparing cost Escalation for three types of project ownership--private, state-owned enterprise and other public ownership--it is shown that the oft-seen claim that public ownership is problematic and private ownership effective in curbing cost Escalation is an oversimplification. Type of accountability appears to matter more to cost Escalation than type of ownership.
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What causes cost overrun in transport infrastructure projects?
Transport Reviews, 2004Co-Authors: Bent Flyvbjerg, Mette K. Skamris Holm, SØren L. BuhlAbstract:Results from the first statistically significant study of the causes of cost Escalation in transport infrastructure projects are presented. The study is based on a sample of 258 rail, bridge, tunnel and road projects worth US$90 billion. The focus is on the dependence of cost Escalation on: (1) the length of the project-implementation phase, (2) the size of the project and (3) the type of project ownership. First, it was found, with very high statistical significance, that cost Escalation was strongly dependent on the length of the implementation phase. The policy implications are clear: decision-makers and planners should be highly concerned about delays and long implementation phases because they translate into risks of substantial cost Escalations. Second, projects have grown larger over time, and for bridges and tunnels larger projects have larger percentage cost Escalations. Finally, by comparing the cost Escalation for three types of project ownership—private, state-owned enterprise and other public ownership—it was shown that the oft-seen claim that public ownership is problematic and private ownership effective in curbing cost Escalation is an oversimplification. The type of accountability appears to matter more to cost Escalation than type of ownership. Cost
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how common and how large are cost overruns in transport infrastructure projects
Transport Reviews, 2003Co-Authors: Bent Flyvbjerg, Mette Skamris K Holm, SØren L. BuhlAbstract:Despite the hundreds of billions of dollars being spent on infrastructure development — from roads, rail and airports to energy extraction and power networks to the Internet — surprisingly little reliable knowledge exists about the performance of these investments in terms of actual costs, benefits and risks. This paper presents results from the first statistically significant study of cost performance in transport infrastructure projects. The sample used is the largest of its kind, covering 258 projects in 20 nations worth approximately US$90 billion (constant 1995 prices). The paper shows with overwhelming statistical significance that in terms of costs transport infrastructure projects do not perform as promised. The conclusion is tested for different project types, different geographical regions and different historical periods. Substantial cost Escalation is the rule rather than the exception. For rail, average cost Escalation is 45% (SD=38), for fixed links (tunnels and bridges) it is 34% (62) and for roads 20% (30). Cost Escalation appears a global phenomenon, existing across 20 nations on five continents. Cost estimates have not improved and cost Escalation not decreased over the past 70 years. Cost estimates used in decision-making for transport infrastructure development are highly, systematically and significantly misleading. Large cost Escalations combined with large standard deviations translate into large financial risks. However, such risks are typically ignored or underplayed in decision-making, to the detriment of social and economic welfare.
Daniela Damian - One of the best experts on this subject based on the ideXlab platform.
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what do support analysts know about their customers on the study and prediction of support ticket Escalations in large software organizations
arXiv: Software Engineering, 2019Co-Authors: Lloyd Montgomery, Daniela DamianAbstract:Understanding and keeping the customer happy is a central tenet of requirements engineering. Strategies to gather, analyze, and negotiate requirements are complemented by efforts to manage customer input after products have been deployed. For the latter, support tickets are key in allowing customers to submit their issues, bug reports, and feature requests. Whenever insufficient attention is given to support issues, however, their Escalation to management is time-consuming and expensive, especially for large organizations managing hundreds of customers and thousands of support tickets. Our work provides a step towards simplifying the job of support analysts and managers, particularly in predicting the risk of escalating support tickets. In a field study at our large industrial partner, IBM, we used a design science methodology to characterize the support process and data available to IBM analysts in managing Escalations. Through iterative cycles of design and evaluation, we translated our understanding of support analysts' expert knowledge of their customers into features of a support ticket model to be implemented into a Machine Learning model to predict support ticket Escalations. We trained and evaluated our Machine Learning model on over 2.5 million support tickets and 10,000 Escalations, obtaining a recall of 79.9% and an 80.8% reduction in the workload for support analysts looking to identify support tickets at risk of Escalation. Further on-site evaluations, through a prototype tool we developed to implement our Machine Learning techniques in practice, showed more efficient weekly support-ticket-management meetings. The features we developed in the Support Ticket Model are designed to serve as a starting place for organizations interested in implementing our model to predict support ticket Escalations, and for future researchers to build on to advance research in ...
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What do Support Analysts Know About Their Customers? On the Study and Prediction of Support Ticket Escalations in Large Software Organizations
2017 IEEE 25th International Requirements Engineering Conference (RE), 2017Co-Authors: Lloyd Montgomery, Daniela DamianAbstract:Understanding and keeping the customer happy is a central tenet of requirements engineering. Strategies to gather, analyze, and negotiate requirements are complemented by efforts to manage customer input after products have been deployed. For the latter, support tickets are key in allowing customers to submit their issues, bug reports, and feature requests. Whenever insufficient attention is given to support issues, however, their Escalation to management is time-consuming and expensive, especially for large organizations managing hundreds of customers and thousands of support tickets. Our work provides a step towards simplifying the job of support analysts and managers, particularly in predicting the risk of escalating support tickets. In a field study at our large industrial partner, IBM, we used a design science methodology to characterize the support process and data available to IBM analysts in managing Escalations. Through iterative cycles of design and evaluation, we translated our understanding of support analysts' expert knowledge of their customers into features of a support ticket model to be implemented into a Machine Learning model to predict support ticket Escalations. We trained and evaluated our Machine Learning model on over 2.5 million support tickets and 10,000 Escalations, obtaining a recall of 79.9% and an 80.8% reduction in the workload for support analysts looking to identify support tickets at risk of Escalation. Further on-site evaluations, through a prototype tool we developed to implement our Machine Learning techniques in practice, showed more efficient weekly support-ticket-management meetings. The features we developed in the Support Ticket Model are designed to serve as a starting place for organizations interested in implementing our model to predict support ticket Escalations, and for future researchers to build on to advance research in Escalation prediction.
Bent Flyvbjerg - One of the best experts on this subject based on the ideXlab platform.
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what causes cost overrun in transport infrastructure projects
arXiv: General Finance, 2013Co-Authors: Bent Flyvbjerg, Mette Skamris K Holm, SØren L. BuhlAbstract:This article presents results from the first statistically significant study of causes of cost Escalation in transport infrastructure projects. The study is based on a sample of 258 rail, bridge, tunnel and road projects worth US$90 billion. The focus is on the dependence of cost Escalation on (1) length of project implementation phase, (2) size of project and (3) type of project ownership. First, it is found with very high statistical significance that cost Escalation is strongly dependent on length of implementation phase. The policy implications are clear: Decision makers and planners should be highly concerned about delays and long implementation phases because they translate into risks of substantial cost Escalations. Second, it is found that projects have grown larger over time and that for bridges and tunnels larger projects have larger percentage cost Escalations. Finally, by comparing cost Escalation for three types of project ownership--private, state-owned enterprise and other public ownership--it is shown that the oft-seen claim that public ownership is problematic and private ownership effective in curbing cost Escalation is an oversimplification. Type of accountability appears to matter more to cost Escalation than type of ownership.
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What causes cost overrun in transport infrastructure projects?
Transport Reviews, 2004Co-Authors: Bent Flyvbjerg, Mette K. Skamris Holm, SØren L. BuhlAbstract:Results from the first statistically significant study of the causes of cost Escalation in transport infrastructure projects are presented. The study is based on a sample of 258 rail, bridge, tunnel and road projects worth US$90 billion. The focus is on the dependence of cost Escalation on: (1) the length of the project-implementation phase, (2) the size of the project and (3) the type of project ownership. First, it was found, with very high statistical significance, that cost Escalation was strongly dependent on the length of the implementation phase. The policy implications are clear: decision-makers and planners should be highly concerned about delays and long implementation phases because they translate into risks of substantial cost Escalations. Second, projects have grown larger over time, and for bridges and tunnels larger projects have larger percentage cost Escalations. Finally, by comparing the cost Escalation for three types of project ownership—private, state-owned enterprise and other public ownership—it was shown that the oft-seen claim that public ownership is problematic and private ownership effective in curbing cost Escalation is an oversimplification. The type of accountability appears to matter more to cost Escalation than type of ownership. Cost
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how common and how large are cost overruns in transport infrastructure projects
Transport Reviews, 2003Co-Authors: Bent Flyvbjerg, Mette Skamris K Holm, SØren L. BuhlAbstract:Despite the hundreds of billions of dollars being spent on infrastructure development — from roads, rail and airports to energy extraction and power networks to the Internet — surprisingly little reliable knowledge exists about the performance of these investments in terms of actual costs, benefits and risks. This paper presents results from the first statistically significant study of cost performance in transport infrastructure projects. The sample used is the largest of its kind, covering 258 projects in 20 nations worth approximately US$90 billion (constant 1995 prices). The paper shows with overwhelming statistical significance that in terms of costs transport infrastructure projects do not perform as promised. The conclusion is tested for different project types, different geographical regions and different historical periods. Substantial cost Escalation is the rule rather than the exception. For rail, average cost Escalation is 45% (SD=38), for fixed links (tunnels and bridges) it is 34% (62) and for roads 20% (30). Cost Escalation appears a global phenomenon, existing across 20 nations on five continents. Cost estimates have not improved and cost Escalation not decreased over the past 70 years. Cost estimates used in decision-making for transport infrastructure development are highly, systematically and significantly misleading. Large cost Escalations combined with large standard deviations translate into large financial risks. However, such risks are typically ignored or underplayed in decision-making, to the detriment of social and economic welfare.
Lloyd Montgomery - One of the best experts on this subject based on the ideXlab platform.
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what do support analysts know about their customers on the study and prediction of support ticket Escalations in large software organizations
arXiv: Software Engineering, 2019Co-Authors: Lloyd Montgomery, Daniela DamianAbstract:Understanding and keeping the customer happy is a central tenet of requirements engineering. Strategies to gather, analyze, and negotiate requirements are complemented by efforts to manage customer input after products have been deployed. For the latter, support tickets are key in allowing customers to submit their issues, bug reports, and feature requests. Whenever insufficient attention is given to support issues, however, their Escalation to management is time-consuming and expensive, especially for large organizations managing hundreds of customers and thousands of support tickets. Our work provides a step towards simplifying the job of support analysts and managers, particularly in predicting the risk of escalating support tickets. In a field study at our large industrial partner, IBM, we used a design science methodology to characterize the support process and data available to IBM analysts in managing Escalations. Through iterative cycles of design and evaluation, we translated our understanding of support analysts' expert knowledge of their customers into features of a support ticket model to be implemented into a Machine Learning model to predict support ticket Escalations. We trained and evaluated our Machine Learning model on over 2.5 million support tickets and 10,000 Escalations, obtaining a recall of 79.9% and an 80.8% reduction in the workload for support analysts looking to identify support tickets at risk of Escalation. Further on-site evaluations, through a prototype tool we developed to implement our Machine Learning techniques in practice, showed more efficient weekly support-ticket-management meetings. The features we developed in the Support Ticket Model are designed to serve as a starting place for organizations interested in implementing our model to predict support ticket Escalations, and for future researchers to build on to advance research in ...
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What do Support Analysts Know About Their Customers? On the Study and Prediction of Support Ticket Escalations in Large Software Organizations
2017 IEEE 25th International Requirements Engineering Conference (RE), 2017Co-Authors: Lloyd Montgomery, Daniela DamianAbstract:Understanding and keeping the customer happy is a central tenet of requirements engineering. Strategies to gather, analyze, and negotiate requirements are complemented by efforts to manage customer input after products have been deployed. For the latter, support tickets are key in allowing customers to submit their issues, bug reports, and feature requests. Whenever insufficient attention is given to support issues, however, their Escalation to management is time-consuming and expensive, especially for large organizations managing hundreds of customers and thousands of support tickets. Our work provides a step towards simplifying the job of support analysts and managers, particularly in predicting the risk of escalating support tickets. In a field study at our large industrial partner, IBM, we used a design science methodology to characterize the support process and data available to IBM analysts in managing Escalations. Through iterative cycles of design and evaluation, we translated our understanding of support analysts' expert knowledge of their customers into features of a support ticket model to be implemented into a Machine Learning model to predict support ticket Escalations. We trained and evaluated our Machine Learning model on over 2.5 million support tickets and 10,000 Escalations, obtaining a recall of 79.9% and an 80.8% reduction in the workload for support analysts looking to identify support tickets at risk of Escalation. Further on-site evaluations, through a prototype tool we developed to implement our Machine Learning techniques in practice, showed more efficient weekly support-ticket-management meetings. The features we developed in the Support Ticket Model are designed to serve as a starting place for organizations interested in implementing our model to predict support ticket Escalations, and for future researchers to build on to advance research in Escalation prediction.
