RMME student, Xiaowen Liu, successfully defended her doctoral dissertation entitled, “The Impact of Missing Data on Parameter Estimation in Computerized Adaptive Testing.” Congratulations, Dr. Liu!
Month: August 2021
Xiaowen Liu Successfully Defends Doctoral Dissertation
RMME student, Xiaowen Liu, successfully defended her doctoral dissertation entitled, “The Impact of Missing Data on Parameter Estimation in Computerized Adaptive Testing.” Congratulations, Dr. Liu!
*Anthony J. Gambino Successfully Defends Doctoral Dissertation
Anthony J. Gambino successfully defended his doctoral dissertation entitled, “Evaluating the Performance of Continuous Analysis of Symmetrically Predicted Endogenous Subgroups.” Congratulations, Dr. Gambino!
Anthony J. Gambino Successfully Defends Doctoral Dissertation
Anthony J. Gambino successfully defended his doctoral dissertation entitled, “Evaluating the Performance of Continuous Analysis of Symmetrically Predicted Endogenous Subgroups.” Congratulations, Dr. Gambino!
*Upcoming RMME/STAT Colloquium (9/10): Susan Murphy, “Assessing Personalization in Digital Health”
RMME/STAT Joint Colloquium
Assessing Personalization in Digital Health
Dr. Susan Murphy
Harvard University
Friday, September 10th, at 12:00PM ET
https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m883b79a16b8b2c21038a80da6301cba3
Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? I discuss some first approaches to addressing these questions.
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Open in new tab Upcoming RMME/STAT Colloquium (9/10): Susan Murphy, “Assessing Personalization in Digital Health”
RMME/STAT Joint Colloquium
Assessing Personalization in Digital Health
Dr. Susan Murphy
Harvard University
Friday, September 10th, at 12:00PM ET
https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m883b79a16b8b2c21038a80da6301cba3
Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? I discuss some first approaches to addressing these questions.
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