Doctoral Dissertation Defense: Aryana Arsham

The field of cost-effectiveness analysis (CEA) addresses the question of  whether a new treatment provides value for the money, compared to a standard treatment. The relevant statistical methodologies are based on outcomes of cost and effectiveness, typically obtained from a randomized controlled trial. These methodologies assist health policy-makers in deciding if a new treatment should be assigned to patients, and what amount of investment can provide cost-effectiveness for the new  treatment.

There are several commonly used metrics to quantify a new treatment's cost-effectiveness, and most of these are population level criteria.  This thesis develops statistical methodologies for CEA that enable decision making at the population level, and also at the individual patient level. The realization that between-patient variability can affect cost-effectiveness has underscored the need to develop criteria for individualized CEA. In addition, there has also been interest in developing  cost-effectiveness metrics when there are multiple effectiveness measures.

The research reported in the thesis deals with  individualized criteria for CEA, as well as aggregate criteria when there are multiple effectiveness measures.