Chronic pain is a multidimensional problem that affects nearly 40 percent of the United States population and is estimated to incur $560-$635 billion in incremental healthcare costs and costs related to rehabilitation and lost productivity annually. With a growing number of treatment options and new medications, formulating an evidence-based, individually-tailored treatment plan has become increasingly complex. This research project develops new statistics-based optimization methods for adaptive interdisciplinary pain management that use patient data to recommend an interdisciplinary treatment regime for controlling pain outcomes. The research involves an interdisciplinary research team from the University of Texas (UT) at Arlington and uses data from the UT Southwestern Medical Center at Dallas. Because UT Southwestern uses a prevalent and standardized dataset, the research will be applicable to approximately 100 pain centers across the nation and will influence how interdisciplinary pain management is implemented.
The research addresses four major topics in statistical optimization. One, the research studies how to coordinate the development of statistical meta-models and optimization algorithms. Specifically, several meta-models will be developed, which are accurate representations of the underlying system and can be globally optimized. The second major topic involves using a learning method to impute missing values in which the missing locations are in a block-wise structure. Three, the research develops an inverse-probability-of-treatment weighted estimators method for complex data. Finally, the research evaluates treatment solutions found from optimizing one prediction model with the other prediction models using simulation. Although this research is on pain management, the developed methodologies of this project can potentially be generalized to create complex adaptive treatment regimes for ailments other than pain.
