Classification-based method for estimating dynamic treatment regimes

Dynamic treatment regimes are sequential decision rules dictating how to individualize treatments to patients based on evolving treatments and covariate history. In this thesis, we investigate two methods of estimating dynamic treatment regimes. The first method extends outcome weighted learning from two-treatments to multi-treatments and allows for negative treatment outcome. We show that under two different sets of assumptions, the Fisher consistency can be maintained. The second method estimates treatment rules by a neural classification tree. A weighted squared loss function is defined to approximate the indicator function to maintain the smoothness. A method of tree reconstruction and pruning is proposed to increase the interpretability. Simulation studies and real application to data from Sequential Treatment Alternatives to Relieve Depression (STAR*D) clinical trial are conducted to illustrate the proposed methods.

Name thesis:degree_name

M Sc

Discipline thesis:degree_discipline

Statistics and Actuarial Sciences

Grantor dc:publisher

The University of Western Ontario

Year dc:date.issued

2020

Author dc:creator

• Shen, Junwei

Advisor dc:contributor.advisor

• He, Wenqing

• Classification methods
• dynamic treatment regimes
• neural classification tree
• outcome weighted learning
• personalized medicine
• support vector machine

Language dc:language.iso

en_ca

Handle dc:identifier.uri

https://hdl.handle.net/20.500.14721/30265

OAI identifier oai:identifier

oai:uwo.scholaris.ca:20.500.14721/30265