New approaches using probabilistic graphical models in health economics and outcomes research

UnrestrictedProbabilistic graphical models (PGMs) are those models that employ both probability theory and graph theory. The fundamental to the idea of a PGM is the notion of modularity, i.e. a complex system can be built by combining simpler parts. Health economics and outcomes research (HEOR) is a multidisciplinary approach to healthcare and research that incorporates number of areas of expertise including clinical research, epidemiology, health services research, economics, and psychometrics. The field has rapidly expanded in the last decade and played a crucial role in improvement the quality of healthcare. Drugs, healthcare programs, and medical devices are increasingly required to demonstrate not only their efficacy and safety characteristics, but also their superior performance in clinical effectiveness, health-related quality of life and economic outcomes. While probabilistic graphical models have become a popular tool for data analysis in health informatics, especially used to prescribe treatment or guide diagnostic decisions, their use and applications in HEOR have been limited. This three-paper dissertation introduces new approaches using probabilistic graphical models in health economics and outcomes research.; Paper 1 demonstrates a cost-effectiveness analysis model of an expensive and newly approved cancer drug, lapatinib, using a Markov model with Monte-Carlo simulation method. This modeling approach innovatively uses Microsoft® Excel spreadsheet with Visual Basic programming language and provides health economists the flexibility to customize, ease to calibrate, and graphical visualization for their cost-effectiveness models. Paper 2 presents an alternative method using a Bayesian network that can detect blood lab errors better than the existing automated models. Successful implementation of the Bayesian network model in clinical laboratory can help to reduce medical costs and improve patient safety. Paper 3 provides a new robust and natural approach using Bayesian networks to map health-profile or disease-specific measures onto preference-based measures. Applying the probabilistic mapping technique to obtain QALYs can be useful in health economic evaluations when health utilities are not directly available