Decision support tools to optimize economic outcomes for type 2 diabetes.

As the costs of type 2 diabetes mellitus (T2DM) care and related clinical trials continue to rise, economically viable methods are being sought to effectively predict the relative utility of various treatment options. The high price of clinical trials has led to the development of alternative methods to collect and consolidate data. Comparative effectiveness research (CER) synthesizes existing evidence to address knowledge gaps and drive patient-focused clinical decisions and outcomes. CER methods compare the health outcomes and costs associated with interventions to determine the option with the maximum patient benefit at optimal cost. In addition to traditional CER approaches such as systematic reviews, meta-analyses, and retrospective claims analyses, Markov modeling and Bayesian analysis can be applied to predict patient outcomes in scenarios where clinical trials are not feasible. Additionally, cost-benefit, cost-effectiveness, and cost-utility analyses comprise "cost-effectiveness analyses." Cost-benefit analysis looks solely at monetary value, while cost-effectiveness and cost-utility analyses include gains in health and quality of life, providing a ratio of cost to benefit. This paper will discuss a range of approaches to CER including Markov modeling, mixed treatment comparisons, the Archimedes model, and Bayesian statistics, and provide guidance in interpreting data from these studies in a managed care context, with a particular focus on evaluating treatments for T2DM. It will also provide guidance on common indices of comorbidity used in health economics research. Data from these models can be used to reduce treatment costs and improve the overall quality of population-level health.