A Therapy Decision Support Model for Early Cancer Detection

Early cancer detection is vital to promoting good quality of life and preventing mortality. In this proposal, we provide a new methodology in the measurement of Pharmacoeconomic outcomes of cancer treatment outcomes with regards to early detection. Conventionally, Pharmacoeconomic outcomes are generally measured by ratios. The most commonly used ratios are the cost-effectiveness, cost-benefit, cost-utility and incremental cost-effectiveness ratios. However, ratios are fundamentally unsuitable to be used measures for the purpose of comparison across drugs or health programs that wildly differ in cost or effectiveness. For example, the cost of innovator brands are many times the cost of the same generic although effectiveness should be theoretically equal. Thus, medical decision making can be a fundamentally flawed process by relying on conventional ratios. Thus, we propose a candidate model that provide a new Pharmacoeconomic index that is symmetrical, proportional and scale invariant. These properties provide the vital invariance required for a broad range of decision making under great uncertainty for medical practitioners and policy makers