Improving efficiency and value in palliative care with net benefit regression: an introduction to a simple method for cost-effectiveness analysis with person-level data.
The objective of this article is to illustrate how to do cost-effectiveness analysis (CEA) using net-benefit regression and to explain how this method provides all of the benefits CEA can provide for improving efficiency and value in palliative care. We use a hypothetical data set with person-level data to demonstrate the net-benefit regression framework. Cost and effect data are combined with assumptions about willingness to pay to produce a net-benefit variable for each study participant. This net-benefit variable is the dependent variable in a net-benefit regression. In the simplest formulation, the regression coefficient on the treatment indicator variable estimates the difference in value between extra benefits and extra costs. The estimate and its confidence interval provide policy-relevant information. Net-benefit regression can be used with data from clinical trials or from administrative data sets. The results can be used to help develop policy, with an aim toward improving efficiency and value in health care.
Hoch
Jeffrey S
JS
Centre for Research on Inner City Health, The Keenan Research Centre, Li Ka Shing Knowledge Institute, St. Michaels Hospital, Ontario, Canada. jeffrey.hoch@utoronto.ca
D002170:Canada / Q000453:epidemiology; D003362:Cost-Benefit Analysis; D017598:Efficiency, Organizational / Q000191:economics; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006801:Humans; D015994:Incidence; D018803:Models, Economic; D010146:Pain / Q000191:economics* / Q000453:epidemiology / Q000517:prevention & control*; D010166:Palliative Care / Q000191:economics* / Q000706:statistics & numerical data*; D012044:Regression Analysis