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Quality adjusted Q-learning and conditional structural mean models for optimizing dynamic treatment regimes

Johnson, Geoffrey (2016) Quality adjusted Q-learning and conditional structural mean models for optimizing dynamic treatment regimes. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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The focus of this work is to investigate a form of Q-learning using estimating equations for quality adjusted survival time, and to generalize these methods to quality adjust other outcomes. We use the m-out-of-n bootstrap and threshold utility analysis to show how the patient-specific optimal regime varies according to treatment characteristics (e.g. cost, side effects). Methodologies investigated are demonstrated to construct optimal treatment regimes for the treatment of children's neuroblastoma. We also propose a new method for optimizing dynamic treatment regimes using conditional structural mean models. The inverse-probability-of-treatment weighted (IPTW) or g-computation estimator is used at each stage to estimate what we call the `preliminary' optimal treatment regime, given patient information up to the current stage and prior treatment assignment. Essentially this tailors the optimal treatment assignment at the current stage, and provides an optimal strategy for the remaining stages given the information currently available. We compare this method for optimizing a dynamic treatment regime to Q-learning. Additionally, we propose a two step prescriptive variable selection procedure that supports the tailored optimization of dynamic treatment regimes using conditional structural mean models by eliminating from consideration any suboptimal treatment regimes and sifting out the covariates that prescribe the optimal treatment regimes. The methods described herein are meant to advance the field of dynamic treatment regimes, a field that has a substantial impact on public health. The treatment policies that come from DTRs, whether determined for the population as a whole or tailored for specific subgroups, can be used to guide and shape health policies that will ultimately lead to greater public health and safety.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Johnson, Geoffreygsj5@pitt.eduGSJ5
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorWahed, Abdus S.wahed@pitt.eduWAHED
Committee MemberJeong, Jong-Hyeonjjeong@pitt.eduJJEONG
Committee MemberChang, Chung-Chou H.changj@pitt.eduCHANGJ
Committee MemberCheng, Yuyucheng@pitt.eduYUCHENG
Date: 9 September 2016
Date Type: Publication
Defense Date: 4 May 2016
Approval Date: 9 September 2016
Submission Date: 29 May 2016
Access Restriction: 3 year -- Restrict access to University of Pittsburgh for a period of 3 years.
Number of Pages: 100
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Dynamic Treatment Regime, Adaptive Treatment Strategy, Quality Adjusted Lifetime, Potential Outcomes, Counterfactuals, Inverse Probability Weighting, m-out-of-n Bootstrap, Structural Mean Model, Q-learning, g-computation
Date Deposited: 09 Sep 2016 19:15
Last Modified: 01 Jul 2019 05:15


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