Abstract Details
Activity Number:
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513
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract #311838
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Title:
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Comparison of the Quality of Care in Trauma with Application to the Prospective, Observational, Multi-Center Massive Transfusion Study (PROMMTT)
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Author(s):
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Anna Decker*+ and Alan Hubbard and Mitchell Cohen
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Companies:
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University of California, Berkeley and University of California, Berkeley and University of California, San Francisco
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Keywords:
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causal inference ;
machine learning
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Abstract:
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Trauma is one of the leading causes of death in the United States. Understanding the underlying mechanism and improving the treatment of trauma is of great clinical and public health interest. The purpose of this analysis was to determine an objective comparison of the quality of care at trauma centers involved in the PRospective, Observational, Multi-center Massive Transfusion sTudy (PROMMTT) using a parameter motivated by the causal inference literature: the effect of treatment among the treated (ETT). We compared a variety of clinical outcomes such as mortality, massive transfusion, and number of days in the hospital among large- and small-volume sites involved in PROMMTT as well as individual sites in the study. Our comparisons rely on a data-adaptive algorithm that allows protects against model misspecification and a bias-reduction step that achieves the optimal bias-variance trade-off. We found that there was a benefit of being treated at large-volume centers as well as particular sites involved in the study. This procedure allows for the objective comparison of the quality of care in trauma patients and highlights the utility of causal inference in a clinical setting.
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Authors who are presenting talks have a * after their name.
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