American Statistical Association
Cost-effectiveness analysis (CEA) is a collection of techniques for structuring comparisons between competing interventions. It can inform decision-making by providing means for optimizing health benefits from a specified budget, or finding the lowest cost strategy for a specified health benefit. Markov processes are useful in modeling the dynamics of patient health outcomes as they unfold over time. States of the process represent health conditions or health states. We use a continuous-time finite-state Markov process to incorporate patient costs as they are incurred during sojourn in health states and in transition from one health state to another. By combining these expenditure streams, the net present value is the discounted expected total cost over a specified time period. Other metrics widely used in CEA such as net health benefit, net health cost and the cost-effectiveness ratio, and measures of health benefit such as life expectancy and quality-adjusted life years are defined as functions of expected values. We outline approaches to estimation of these summary statistics from health outcome and cost data that might be incompletely ascertained in some patients. Regression models are used to incorporate patient-specific demographic and clinical characteristics and their impact on the metrics used in CEA can be assessed.
Joseph Gardiner, Ph.D. is Interim Chairperson and Director of the Division of Biostatistics, Department of Epidemiology at Michigan State University (MSU). Dr. Gardiner has been a faculty member at MSU since 1978 where his primary research interests are in outcomes research and cost-effectiveness analysis in the area of cardiovascular disease. He has published widely on both methodological aspects of statistics, as well as in many applied areas.
|Date:||Thursday, May 20, 2010|
|Time:||4:00 - 5:00 P.M.|
Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Computer Lab
6th Floor - Room 656
New York, New York