RSS Feeds
Managing People at High Risk for Diabetes
- William H. Herman, MD, MPH;
- Thomas J. Hoerger, PhD;
- Katherine Hicks, MS;
- Michael Brandle, MD, MS;
- Stephen W. Sorensen, PhD;
- Ping Zhang, PhD;
- Michael M. Engelgau, MD, MS;
- Richard F. Hamman, MD, DrPH;
- David G. Marrero, PhD;
- Ronald T. Ackermann, MD, MPH; and
- Robert E. Ratner, MD
- From the University of Michigan Health System, Ann Arbor, MI 48109; RTI International, Research Triangle Park, NC 27599; Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland; Centers for Disease Control and Prevention, Atlanta, GA 30341; University of Colorado Health Sciences Center, Denver, CO 80262; Indiana University School of Medicine, Indianapolis, IN 46202; and Medstar Research Institute, Hyattsville, MD 20783.
TO THE EDITOR:
We are writing in response to the report by Eddy and colleagues (1). We have several questions about their simulations, conclusions, and representation of our work. Unlike the authors, we found a favorable cost-effectiveness ratio for the Diabetes Prevention Program lifestyle intervention (2).
The Archimedes model may or may not represent a quantum leap in diabetes modeling. For simulation models to aid decision making, they must be transparent and have face validity. By using complex differential equations fitted to empirical data, the Archimedes model can simulate an infinite number of physiologic processes. Unfortunately, the equations governing disease progression are not transparent. In contrast, Markov models can simulate only a finite number of health states. Yet, as demonstrated in the appendix to the report by Eddy and colleagues, Markov models are transparent and invite critics to debate their validity.
In this simulation, unlike previous simulations, the authors constrain the progression of hyperglycemia to reflect the increase in average fasting plasma glucose levels over the first 4 years …
