Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Click here to browse AJSM online!

Sign In to gain access to subscriptions and/or personal tools.
Medical Decision Making
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Lee, C. P.
Right arrow Articles by Zenios, S. A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Lee, C. P.
Right arrow Articles by Zenios, S. A.
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

A Simulation Model to Estimate the Cost and Effectiveness of Alternative Dialysis Initiation Strategies

Chris P. Lee, PhD

Operations and Information Management Department, The Wharton School, University of Pennsylvania, PA

Glenn M. Chertow, MD, MPH

Division of Nephrology, Department of Medicine, University of California San Francisco, CA

Stefanos A. Zenios, PhD

Graduate School of Business, Stanford University, CA, stefzen{at}stanford.edu

Background. Patients with end-stage renal disease (ESRD) require dialysis to maintain survival. The optimal timing of dialysis initiation in terms of cost-effectiveness has not been established.

Methods. We developed a simulation model of individuals progressing towards ESRD and requiring dialysis. It can be used to analyze dialysis strategies and scenarios. It was embedded in an optimization frame worked to derive improved strategies.

Results. Actual (historical) and simulated survival curves and hospitalization rates were virtually indistinguishable. The model overestimated transplantation costs (10%) but it was related to confounding by Medicare coverage. To assess the model's robustness, we examined several dialysis strategies while input parameters were perturbed. Under all 38 scenarios, relative rankings remained unchanged. An improved policy for a hypothetical patient was derived using an optimization algorithm.

Conclusion. The model produces reliable results and is robust. It enables the cost-effetiveness analysis of dialysis strategies.

Key Words: chronic kidney disease • cost-effectiveness analysis • dialysis • computer simulation • Monte Carlo method

Medical Decision Making, Vol. 26, No. 5, 535-549 (2006)
DOI: 10.1177/0272989X06290488
Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?


This article has been cited by other articles:


Home page
 Operations ResearchHome page
C. P. Lee, G. M. Chertow, and S. A. Zenios
Optimal Initiation and Management of Dialysis Therapy
Operations Research, November 1, 2008; 56(6): 1428 - 1449.
[Abstract] [PDF]

Home page
 J. Am. Soc. Nephrol.Home page
C. P. Lee, S. A. Zenios, and G. M. Chertow
Cost-Effectiveness of Frequent In-Center Hemodialysis
J. Am. Soc. Nephrol., September 1, 2008; 19(9): 1792 - 1797.
[Abstract] [Full Text] [PDF]