Our society is continually faced with difficult decisions when allocating resources and containing costs. Cost-effectiveness analysis (CEA) is an economic study that compares the relative expenditures and outcomes of multiple strategies for performing the same task. The use of CEA in various fields, including economic, social, biomedical, and public health sciences, has been popular for many years. [43,895 articles found in PubMed using """"""""cost-effectiveness analysis""""""""]. Statistical methods for CEA have been extensively developed, and some measures have been widely adopted. However, it is clear that CEA remains controversial despite its long history of use and the efforts that have been made to understand various aspects of it. [For example, more than 20 methodology papers have been published on one statistical problem: deriving or comparing confidence intervals for the incremental cost-effectiveness ratio. This level of attention and publication is highly unusual in other statistical problems/settings.] Moreover, many journals have written publication policies on CEA. This is primarily due to the fact that: 1) different methods for CEA can yield different, often counterintuitive or inconsistent results;2) there is uncertainty and difficulty in interpreting the analysis that was performed;3) there is a lack of consensus on methods;and 4) there is a huge impact that CEA may have on decision making. As such, most of the existing analyses require great caution and care before, during and even after the analysis. We claim that the current standard approach for CEA is suboptimal and can be problematic, as it is based on only one analytical perspective and does not account for some important methodological issues. In this proposal, we intend to address these aspects of CEA and to develop methodologies and computer software in a unified framework. We wish to stress that multiple analytic methods should be chosen and evaluated together before one makes a conclusive statement about the cost-effectiveness of different strategies. The proposed methods do not compete with one another but are complementary, because they fulfill different tasks and would collectively provide a more comprehensive perspective for economic evaluations.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
Project #
Application #
Study Section
Health Care Quality and Effectiveness Research (HQER)
Program Officer
Kaufmann, Peter G
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Weill Medical College of Cornell University
Public Health & Prev Medicine
Schools of Medicine
New York
United States
Zip Code
Bang, Heejung; Zhao, Hongwei (2016) Median-based incremental cost-effectiveness ratios with censored data. J Biopharm Stat 26:552-64
Bang, Heejung; Zhao, Hongwei (2014) Cost-effectiveness analysis: a proposal of new reporting standards in statistical analysis. J Biopharm Stat 24:443-60
Chen, Shuai; Zhao, Hongwei (2013) Estimating incremental cost-effectiveness ratios and their confidence intervals with different terminating events for survival time and costs. Biostatistics 14:422-32
Chen, Shuai; Zhao, Hongwei (2013) Generalized Redistribute-to-the-Right Algorithm: Application to the Analysis of Censored Cost Data. J Stat Theory Pract 7:
Bang, Heejung; Zhao, Hongwei (2012) Median-Based Incremental Cost-Effectiveness Ratio (ICER). J Stat Theory Pract 6:428-442
Zhao, Hongwei; Zuo, Chen; Chen, Shuai et al. (2012) Nonparametric inference for median costs with censored data. Biometrics 68:717-25
Bang, Heejung; Zhao, Hongwei (2012) Average cost-effectiveness ratio with censored data. J Biopharm Stat 22:401-15
Zhao, H; Cheng, Y; Bang, H (2011) Some insight on censored cost estimators. Stat Med 30:2381-8