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Activity Number: 290 - Contributed Poster Presentations: ENAR
Type: Contributed
Date/Time: Tuesday, August 9, 2022 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract #320972
Title: Adaptive Learning of Relevant Questions from a Questionnaire via Best Subset Algorithms
Author(s): Leyao Zhang* and Wen Wang and Mengtong Hu and Alan Baptist and Peter Song
Companies: University of Michigan, Ann Arbor and University of Michigan, Ann Arbor and University of Michigan, Ann Arbor and University of Michigan, Ann Arbor and University of Michigan
Keywords: Dimension reduction; Mixed integer programming; Quality of life; Regression analysis; Supervised learning
Abstract:

Questionnaire is one of the most popular instruments to measure variables relevant to certain traits of interest. In many practical studies, the scope of a questionnaire is unfit to a new study population that appears different from the original population used for either questionnaire development or validation. Thus, items in a questionnaire may or may not be relevant to the new study population. We consider a supervised learning method to identify a subset of questions whose summary score is maximally associated with the outcome. The resultant set of selected items gives an optimal summary metric of the questionnaire, which improves both statistical power and clinical interpretation. Our item extraction procedure is built upon the best subset algorithm implemented by a mixed integer programming, which enjoys both theoretical guarantee of selection consistency and flexibility of handling non-responses. This best subset algorithm is evaluated by simulations and then applied to a cohort study of older asthma patients to derive tailored quality of life (QoL) scores adaptive to two clinical outcomes of lung function measure (FEV1) and asthma control test (ACT), respectively.


Authors who are presenting talks have a * after their name.

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