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 state transition and outcome models estimated from actual patient data. The sequential treatment structure of the data leads to a form of endogeneity. Results are presented using data from Eugene McDermott Center for Pain Management at the University of Texas Southwestern Medical Center at Dallas. This research develops a process based on the inverse probability of treatment weighted method to address the endogeneity while estimating state transition and outcome models. Two different datasets are used in this study. It was observed that the earlier work done on the smaller dataset had all the treatment variables independent. However the new dataset which has more observations has correlated treatments. This study presents results from the earlier case where the treatments were independent and extends the framework further to develop a general approach to handle correlated treatments.