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" Statistical Methods for Optimal Dose Finding "
Zhu, Liangyu
Lu, Wenbin;Song, Rui
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Latin Dissertation
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| Language of Document
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English
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| Record Number
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1114050
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| Doc. No
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TLpq2400538867
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| Main Entry
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Lu, Wenbin
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Zhu, Liangyu
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| Title & Author
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Statistical Methods for Optimal Dose Finding\ Zhu, LiangyuLu, Wenbin;Song, Rui
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| College
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North Carolina State University
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| Date
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2020
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| student score
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2020
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| Degree
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Ph.D.
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| Page No
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107
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| Abstract
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Statistical methods are becoming increasingly popular for optimizing drug doses in clinical trials. In a typical dose-finding trial, a single optimal dose is decided at the end of the trial and is recommended to all future patients. However, patients might respond differently to the same dose of a drug due to differences in their physical conditions, genetic factors, environmental factors and medication history. Taking patient heterogeneity into consideration when making dose decisions is essential for achieving better treatment results. Traditionally, personalized treatment finding process requires repeating clinical visits of the patient and frequent adjustments of the dosage. Thus the patient is constantly exposed to the risk of underdosing and overdosing during the process. Data driven methods for finding optimal personalized dosage have the potential to shorten the process and lower the risk for the patient. Existing statistical methods for finding personalized treatments are mostly restricted to a finite number of treatment options. In this dissertation, we study the statistical methods for finding the optimal personalized treatment when the treatment options are continuous. The problem is studied under the single-stage setting and the mobile health setting.
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Clinical trials
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Optimal dose
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Statistics
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| Added Entry
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Song, Rui
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