Cancer trials often start investigational therapy at diagnosis or after a selected number of relapses.These are the usual core inclusion criteria in clinical trials.Hence it is helpful when planning a trial to know th...Cancer trials often start investigational therapy at diagnosis or after a selected number of relapses.These are the usual core inclusion criteria in clinical trials.Hence it is helpful when planning a trial to know the likely percentages of patients receiving standard therapy at clinics and hospitals who meet this key inclusion criteria of being newly diagnosed during a period or having just had their first,second or third relapse during an anticipated enrollment time frame.Often regulatory agencies will have approvals tied to the use of a therapy in a relapsed context or in a newly diagnosed context.We provide details on calculations to help those in clinical trial operations make realistic assessments on the number of sites and likely enrollment at clinical trial sites,and the enrollment time frames that might be needed to complete planned total patient enrollment.The estimates complement site feasibility questionnaires which are often sent to gauge patient availability and site interest.展开更多
Recently, negative databases （NDBs） are proposed for privacy protection. Similar to the traditional databases, some basic operations could be conducted over the NDBs, such as select, intersection, update, delete and...Recently, negative databases （NDBs） are proposed for privacy protection. Similar to the traditional databases, some basic operations could be conducted over the NDBs, such as select, intersection, update, delete and so on. However, both classifying and clustering in negative databases have not yet been studied. Therefore, two algorithms, i.e., a k nearest neighbor （kNN） classification algorithm and a k-means clustering algorithm in NDBs, are proposed in this paper, respectively. The core of these two algorithms is a novel method for estimating the Hamming distance between a binary string and an NDB. Experimental results demonstrate that classifying and clustering in NDBs are promising.展开更多
文摘Cancer trials often start investigational therapy at diagnosis or after a selected number of relapses.These are the usual core inclusion criteria in clinical trials.Hence it is helpful when planning a trial to know the likely percentages of patients receiving standard therapy at clinics and hospitals who meet this key inclusion criteria of being newly diagnosed during a period or having just had their first,second or third relapse during an anticipated enrollment time frame.Often regulatory agencies will have approvals tied to the use of a therapy in a relapsed context or in a newly diagnosed context.We provide details on calculations to help those in clinical trial operations make realistic assessments on the number of sites and likely enrollment at clinical trial sites,and the enrollment time frames that might be needed to complete planned total patient enrollment.The estimates complement site feasibility questionnaires which are often sent to gauge patient availability and site interest.
基金This work was partly supported by the National Natural Science Foundation of China （Grant＇No. 61175045）.
文摘Recently, negative databases （NDBs） are proposed for privacy protection. Similar to the traditional databases, some basic operations could be conducted over the NDBs, such as select, intersection, update, delete and so on. However, both classifying and clustering in negative databases have not yet been studied. Therefore, two algorithms, i.e., a k nearest neighbor （kNN） classification algorithm and a k-means clustering algorithm in NDBs, are proposed in this paper, respectively. The core of these two algorithms is a novel method for estimating the Hamming distance between a binary string and an NDB. Experimental results demonstrate that classifying and clustering in NDBs are promising.
