Testing Equality of Average Cost-Effectiveness Ratios in Multiple Treatments

• Activity Number: 446
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
• Sponsor: International Chinese Statistical Association
• Author(s): Tsai-Yu Lin* and Chi-Rong Li
• Companies: Feng Chia University and Chung Shan Medical University
• Keywords: Average cost-effectiveness ratio ; Cost-effectiveness analysis ; Generalized p-value ; Incremental cost-effectiveness ratio

Abstract

Cost-effectiveness analysis (CEA) is a form of economic analysis that compares the relative expenditures and outcomes of competing strategies. It has been frequently used in economic, biomedical, social sciences, public health, and other fields over the past several decades. The purpose of many cost-effectiveness evaluations is to compare two treatments for decision making traditionally. In practical, to utilize CEA methods for multiple treatments decision making are needed. Besides, owing to the inherent or statistical complexities of the frequently used measures in the references the decision cannot be made effectively in some situations. In this paper, the average cost-effectiveness ratio (ACER) is as an alternative in comparing multiple treatments. We develop testing procedures to evaluate the equality of several ACERs. Our testing procedures mainly follow the same vein as the generalized variable approach based on the concept of generalized p-value. Moreover, detailed statistical simulation studies will be conducted to evaluate the effectiveness by using their empirical size and power of the two proposed procedures.