The multiprocessor SAS framework for modeling and cost-effectiveness analysis of treatments for cardiovascular disease

Abstract

This thesis provides an economic and mathematical framework, and the computing tools to compare the effects, costs and incremental cost-effectiveness of acute or preventative interventions for cardiovascular disease. A Finite Space Markov Chain Decision Analysis Model is designed by integrating a Decision Trees Model and a Markov Chain Model. The model and Cost-Effectiveness Analysis are implemented by using SAS/IML both on a PC with one processor and on a machine with multiple processors of the High Performance Computing Virtual Laboratory. A sample case with four states and eight intervention policies is studied to illustrate the framework, which is composed of (1) life path simulation, (2) cost and effectiveness estimation, (3) cost-effectiveness analysis, (4) sensitivity analysis, and (5) performance analysis on different platforms. Solution of delay effects, correlation among risk factors, and fluctuation in discount rate are viewed as limitations of the thesis and rewarding areas for further research.