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Activity Number:
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273
- Statistical Methods for Causal Inference and Personalized Medicine Based on Observational Data
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Type:
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Topic-Contributed
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Date/Time:
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Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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ENAR
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Abstract #317227
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Title:
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Personalized Treatment Selection Using Observational Data
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Author(s):
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Sudaraka Tholkage* and KB Kulasekara and Maiying Kong
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Companies:
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University of Louisville and University of Louisville and University of Louisville
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Keywords:
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Observational studies;
Design variables;
Personalized treatments;
Propensity scores
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Abstract:
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Estimating the optimal treatment regime based on individual patient characteristics has been a topic of discussion in many forums. Advanced computational power has added momentum to this discussion over the last two decades and practitioners have been advocating the use of new methods in determining the best treatment. Treatments that are geared towards the “best” outcome for a patient based on his/her genetic markers and characteristics are of high importance. In this article, we develop an approach to predict the optimal personalized treatment based on observational data. We have used inverse probability of treatment weighted machine learning methods to obtain score functions to predict the optimal treatment. Extensive simulation studies showed that our proposed method has desirable performance in selecting the optimal treatment. We provided a case study to examine the Statin use on cognitive function to illustrate the use of our proposed method.
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Authors who are presenting talks have a * after their name.
