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Activity Number: 394 - Challenges and New Directions in Precision Medicine for Large-Scale and Complex Data
Type: Invited
Date/Time: Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
Sponsor: Biometrics Section
Abstract #308065
Title: Depth Importance in Precision Medicine (DIPM): A Tree- and Forest-Based Method for Right-Censored Survival Outcomes
Author(s): Victoria Chen and Heping Zhang*
Companies: Yale University and Yale University
Keywords: Classification tree; Precision medicine; Random forest; Survival outcomes; Variable importance; Right-censored

Many studies have been conducted to compare survival outcomes between interventions. Such comparisons are typically made on the basis of the entire group receiving one intervention versus the others. To identify subgroups for which the preferential treatment may differ from the overall groups, we propose the Depth Importance in Precision Medicine (DIPM) method for such data. The approach first modifies the split criteria of the traditional classification tree to fit the precision medicine setting. Then, a random forest of trees is constructed at each node. The forest is used to calculate depth variable importance scores for each candidate split variable. The variable with the highest score is identified as the best variable to split the node. The importance score is a flexible and simply constructed measure that makes use of the observation that more important variables tend to be selected closer to the root nodes of trees. The DIPM method is primarily designed for the analysis of clinical data with two treatment groups. The DIPM method yields promising results that demonstrate its capacity to guide personalized treatment decisions with survival outcomes.

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

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