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Activity Number: 125 - Novel Approaches for Estimating and Evaluating Treatment Rules with Applications in Mental Health Research
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 1:00 PM to 2:50 PM
Sponsor: Mental Health Statistics Section
Abstract #312466
Title: Confidence in Optimal Personalized Treatment Decisions: A Strategy for Sequential Patient Assessment
Author(s): Nina Orwitz* and Eva Petkova and Thaddeus Tarpey
Companies: NYU School of Medicine and NYU School of Medicine and NYU School of Medicine
Keywords: treatment decision rules; precision medicine

New and evolving medical technologies have motivated the development of treatment decision rules (TDRs) that incorporate complex, expensive data such as imaging (e.g. EEGs). In practice, we aim for TDRs to be valuable for physicians and patients, such that we reduce unnecessary, costly testing while still assigning decisions. For a given TDR, a patient's characteristics lie some distance from the optimal decision boundary separating treatment classes. We expect less certainty or confidence in decisions for patients near the boundary, and more certainty for those farther from the boundary. We propose measuring confidence by estimating the probability of ultimately receiving a particular treatment decision given a patient’s data, as well as the probability that a patient’s final decision will agree with the current decision. As a patient's data accumulates, the decision is sequentially updated and the probabilities are reassessed until high confidence is achieved. We present results from extensive simulation studies and discuss the relationship between probability and distance to a decision boundary. Lastly, we give recommendations for practical use of the confidence measures.

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

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