Graduation Semester and Year
2018
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Industrial Engineering
Department
Industrial and Manufacturing Systems Engineering
First Advisor
Victoria Chen
Abstract
Interdisciplinary pain management combines multiple disciplines of professionals to understand the biological and psychosocial factors causing a patient's pain and to determine the best treatments among many to administer. The Eugene McDermott Center of Pain at University of Texas at Southwestern Medical Center in Dallas runs a two stage adaptive interdisciplinary pain management program with the aim to improve current and future pain outcomes. The sequential treatment regime for the pain and the observational nature of data yield to time varying confounding and a form of endogeneity. This yields biased estimates of the treatment effects which is undesirable. Our adaptive interdisciplinary pain management framework employs state transition and outcome models estimated from actual patient data in the program. This research develops a framework based on the inverse probability of treatment weighting technique to address endogeneity while estimating state transition and outcome models. The parameter estimates obtained from these models are unbiased and can be interpreted as causal effects of the treatments.
Keywords
Time varying confounding, Inverse probability of treatment weighting, Adaptive treatment strategies, Pain management
Disciplines
Engineering | Operations Research, Systems Engineering and Industrial Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Ohol, Nilabh, "Adjusting for Time Varying Confounding in Adaptive Interdisciplinary Pain Management Program" (2018). Industrial, Manufacturing, and Systems Engineering Dissertations. 169.
https://mavmatrix.uta.edu/industrialmanusys_dissertations/169
Comments
Degree granted by The University of Texas at Arlington