Abstract Number:
2221
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Chathura Siriwardhana (1), Bakeerathan Gunaratnam (2), K.B. Kulasekera (2)
Institutions:
(1) University of Hawaii, N/A, (2) University of Louisville, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
We present an innovative approach for tailoring treatment selection on an individualized basis in the presence of correlated multiple responses, particularly those measured on ordinal scales, including binary responses. Our methodology involves the utilization of rank lists for treatments, generated from probabilities of observing responses of higher order than each level of the ordinal outcome, conditional on patient covariate measurements. We introduce a rank aggregation technique designed to amalgamate multiple lists of ranks, allowing for correlations both within these lists and among elements within each list. Our approach is versatile, accommodating any number of treatments and responses, and is applicable across a wide range of models. Our method offers flexibility by allowing the integration of response weights, enabling customization based on patient and clinician preferences on an individual case basis for optimal treatment decisions. To evaluate the performance of our proposed method in finite samples, we conducted a simulation study. Furthermore, we provide two illustrative examples using data from clinical trials on Cystic Fibrosis and Alzheimer's Disease.
Keywords:
Personalized Treatments|Semiparametric Regression|Rank Aggregation|Ordinal Responses| |
Sponsors:
Biometrics Section
Tracks:
Personalized/Precision Medicine
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