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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 #317656
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Title:
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Optimal Personalized Treatment Selection with Multiple Outcomes
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Author(s):
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Susmita Datta*
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Companies:
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University of Florida
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Keywords:
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Abstract:
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In this work, we propose a method for individualized treatment selection based on observational data for the K treatment (K>2) case. Our method is based on patient specific index values constructed from collected covariate measurements and estimated propensity scores. Our method covers any number of treatments and outcome variables, and it can be applied for a broad set of models. The proposed method uses a rank aggregation technique to estimate an ordering of treatments based on ranked lists of smoothed conditional means of outcome measures. The method has the flexibility to incorporate patient and clinician preferences to the optimal treatment decision on an individual case basis. A simulation study demonstrates the performance of the proposed method in finite samples.
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Authors who are presenting talks have a * after their name.
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