Generation of Individualized Treatment Decision Tree Algorithm with Application to Randomized Control Trials and Electronic Medical Record Data

Persistent Link:
http://hdl.handle.net/10150/613559
Title:
Generation of Individualized Treatment Decision Tree Algorithm with Application to Randomized Control Trials and Electronic Medical Record Data
Author:
Doubleday, Kevin
Issue Date:
2016
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
With new treatments and novel technology available, personalized medicine has become a key topic in the new era of healthcare. Traditional statistical methods for personalized medicine and subgroup identification primarily focus on single treatment or two arm randomized control trials (RCTs). With restricted inclusion and exclusion criteria, data from RCTs may not reflect real world treatment effectiveness. However, electronic medical records (EMR) offers an alternative venue. In this paper, we propose a general framework to identify individualized treatment rule (ITR), which connects the subgroup identification methods and ITR. It is applicable to both RCT and EMR data. Given the large scale of EMR datasets, we develop a recursive partitioning algorithm to solve the problem (ITR-Tree). A variable importance measure is also developed for personalized medicine using random forest. We demonstrate our method through simulations, and apply ITR-Tree to datasets from diabetes studies using both RCT and EMR data. Software package is available at https://github.com/jinjinzhou/ITR.Tree.
Type:
text; Electronic Thesis
Keywords:
Random Forest; Recursive Partitioning; Subgroup Identification; Value Function; Variable Importance; Biostatistics; Personalized Medicine
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College; Biostatistics
Degree Grantor:
University of Arizona
Advisor:
Zhou, Jin

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleGeneration of Individualized Treatment Decision Tree Algorithm with Application to Randomized Control Trials and Electronic Medical Record Dataen_US
dc.creatorDoubleday, Kevinen
dc.contributor.authorDoubleday, Kevinen
dc.date.issued2016-
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.description.abstractWith new treatments and novel technology available, personalized medicine has become a key topic in the new era of healthcare. Traditional statistical methods for personalized medicine and subgroup identification primarily focus on single treatment or two arm randomized control trials (RCTs). With restricted inclusion and exclusion criteria, data from RCTs may not reflect real world treatment effectiveness. However, electronic medical records (EMR) offers an alternative venue. In this paper, we propose a general framework to identify individualized treatment rule (ITR), which connects the subgroup identification methods and ITR. It is applicable to both RCT and EMR data. Given the large scale of EMR datasets, we develop a recursive partitioning algorithm to solve the problem (ITR-Tree). A variable importance measure is also developed for personalized medicine using random forest. We demonstrate our method through simulations, and apply ITR-Tree to datasets from diabetes studies using both RCT and EMR data. Software package is available at https://github.com/jinjinzhou/ITR.Tree.en
dc.typetexten
dc.typeElectronic Thesisen
dc.subjectRandom Foresten
dc.subjectRecursive Partitioningen
dc.subjectSubgroup Identificationen
dc.subjectValue Functionen
dc.subjectVariable Importanceen
dc.subjectBiostatisticsen
dc.subjectPersonalized Medicineen
thesis.degree.nameM.S.en
thesis.degree.levelmastersen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineBiostatisticsen
thesis.degree.grantorUniversity of Arizonaen
dc.contributor.advisorZhou, Jinen
dc.contributor.committeememberHu, Chengchengen
dc.contributor.committeememberHu,Chiu-Hsiehen
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