Decision making for disease treatment : operations research and data analytic modeling
Keywords: Management Science and Engineering.

This dissertation focuses on developing and applying various methodologies including operations research and data analytics to solve important problems in healthcare decision making. Healthcare decision making is interesting and challenging because of the probabilistic nature of many healthcare decision problems and because of the range of decision makers involved (e.g., individual patients, clinicians, and policy makers). Healthcare decision making occurs at two levels: clinical decision making at the individual level and policy decision making at the population level. In clinical decision making, practitioners aim to determine which patient needs what and when. The decision can be a simple diagnosis such as predicting arthritis from MRI images or a series of decisions throughout the treatment duration such as HIV treatment management. In policy decision making, policy makers aim to assess decisions undertaken to achieve specific population-level healthcare goals \cite{WHO_HEALTH_2019}. Problems in this field range from disease modeling to policy implementation. In Chapter 2, to extend the boundary of current methodologies in clinical decision making, I develop a theoretical sequential decision making framework, a quantile Markov decision process (QMDP), based on the traditional Markov decision process (MDP). The QMDP model optimizes a specific quantile of the cumulative reward instead of its expectation. I provide analytical results characterizing the optimal QMDP value function and present a dynamic programming-based algorithm to solve for the optimal policy. The algorithm also extends to the MDP problem with a conditional value-at-risk (CVaR) objective. Using the QMDP framework, patients' risk attitudes can be incorporated into the decision making process, thereby enabling patient-centered care. I apply the QMDP framework to an HIV treatment initiation problem, where patients aim to balance the potential benefits and risks of the treatment. In Chapter 3, to inform public health policy regarding treatment for HIV-infected individuals with clinical depression, I develop a microsimulation model of HIV disease and care in Uganda which captures individuals' depression status and the relationship between depression and HIV behaviors. I consider a strategy of screening for depression and providing antidepressant therapy with fluoxetine at initiation of antiretroviral therapy or re-initiation (if a patient has dropped out). I use the model to estimate the effectiveness and cost-effectiveness of such strategies. I show that screening for and treating depression among people living with HIV in sub-Saharan Africa with fluoxetine would be effective in improving HIV treatment outcomes and would be highly cost-effective. In Chapter 4, with the aim of improving policy implementation, I examine the problem of simplifying complex healthcare decision models using metamodeling. Many healthcare decision models, particularly those involving simulation of patient outcomes, are highly complex and may be difficult to use for practical decision making. A metamodel is a simplified version of a more complex model which approximates the relationships between model inputs and outputs, and thus can be used as a surrogate for the more complex model. I develop a framework for metamodeling of simulation models with multivariate outcomes. I apply the methodology to simplify a complex simulation model that evaluates strategies for hepatitis C virus (HCV) screening and treatment in correctional settings. Chapter 5 concludes with discussion of promising areas for further research