TY - GEN
T1 - Outcome and state transition modeling for adaptive interdisciplinary pain management
AU - LeBoulluec, Aera K.
AU - Zeng, Li
AU - Chen, Victoria C.P.
AU - Rosenberger, Jay M.
AU - Gatchel, Robert J.
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2013
Y1 - 2013
N2 - Pain management is a major global health problem. The World Health Organization estimates that, globally, 1 in 5 adults suffer from chronic pain and in the United States alone; chronic pain affects nearly 100 million adults resulting in an estimated annual cost of $560 to $635 billion. The University of Texas at Arlington and the Eugene McDermott Center for Pain Management at the University of Texas Southwestern Medical Center at Dallas (The Center) are collaborating to seek adaptive treatment strategies for interdisciplinary pain management in a two-stage program. 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. To improve current and future pain outcomes, our adaptive interdisciplinary pain management framework employs approximate dynamic programming with state transition and outcome models estimated from actual patient data. The sequential treatment structure of the data leads to a form of endogeneity. This research develops a process based on the inverse-probability-of-treatment weighted (IPTW) method to address the endogeneity while estimating state transition and outcome models. Results are presented using data from the Center.
AB - Pain management is a major global health problem. The World Health Organization estimates that, globally, 1 in 5 adults suffer from chronic pain and in the United States alone; chronic pain affects nearly 100 million adults resulting in an estimated annual cost of $560 to $635 billion. The University of Texas at Arlington and the Eugene McDermott Center for Pain Management at the University of Texas Southwestern Medical Center at Dallas (The Center) are collaborating to seek adaptive treatment strategies for interdisciplinary pain management in a two-stage program. 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. To improve current and future pain outcomes, our adaptive interdisciplinary pain management framework employs approximate dynamic programming with state transition and outcome models estimated from actual patient data. The sequential treatment structure of the data leads to a form of endogeneity. This research develops a process based on the inverse-probability-of-treatment weighted (IPTW) method to address the endogeneity while estimating state transition and outcome models. Results are presented using data from the Center.
KW - Causal effect
KW - Endogeneity
KW - Inverse probability of treatment weighting
KW - Outcome and state transition modeling
UR - http://www.scopus.com/inward/record.url?scp=84900331930&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84900331930&origin=recordpage
M3 - RGC 32 - Refereed conference paper (with host publication)
T3 - IIE Annual Conference and Expo 2013
SP - 1400
EP - 1408
BT - IIE Annual Conference and Expo 2013
PB - Institute of Industrial Engineers
T2 - IIE Annual Conference and Expo 2013
Y2 - 18 May 2013 through 22 May 2013
ER -