Organ transplantation policy evaluation

Authors:
A. Alan B. Pritsker;David L. Martin;Janet S. Reust;Mary Ann Wagner;O. Patrick Daily;Ann M. Harper;Erick B. Edwards;Leah E. Bennett;James R. Wilson;Michael E. Kuhl;John P. Roberts;Margaret D. Allen;James F. Burdick
Affiliations:
Pritsker Corporation, 8910 Purdue Road, Suite 680, Indianapolis, Indiana;Pritsker Corporation, 8910 Purdue Road, Suite 680, Indianapolis, Indiana;Pritsker Corporation, 8910 Purdue Road, Suite 680, Indianapolis, Indiana;Pritsker Corporation, 8910 Purdue Road, Suite 680, Indianapolis, Indiana;United Network for Organ Sharing, 1100 Boulders Parkway, Suite 500, Richmond, Virginia;United Network for Organ Sharing, 1100 Boulders Parkway, Suite 500, Richmond, Virginia;United Network for Organ Sharing, 1100 Boulders Parkway, Suite 500, Richmond, Virginia;United Network for Organ Sharing, 1100 Boulders Parkway, Suite 500, Richmond, Virginia;Pritsker Corporation, North Carolina State University, Department of Industrial Engineering, Raleigh, North Carolina;Pritsker Corporation, North Carolina State University, Department of Industrial Engineering, Raleigh, North Carolina;University of California, San Francisco, Liver Transplant Services, 505 Parnassus Avenue, San Francisco, California;University of Washington Medical Center, 1959 North East Pacific, Box 356310, Seattle, Washington;The Johns Hopkins University, 600 North Wolfe Street, Baltimore, Maryland
Venue:
WSC '95 Proceedings of the 27th conference on Winter simulation
Year:
1995

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Abstract

The paper on the UNOS Liver Allocation Model (ULAM) describes the building of a simulation model that supports policy evaluation for a national medical problem. The modeling and simulation techniques used in building ULAM include: fitting donor and patient arrival processes having trend and cyclic rate components using non-homogeneous Poisson processes (NHPPs) having exponential rate functions which may include both a polynomial and some trigonometric components, fitting distributions to data on transition times between states of medical urgency; application of variance reduction techniques using common random-number streams and prior information; organizing data structures for efficient file searching and ranking capabilities; the use of bootstrapping techniques for attribute sampling; the building of submodels employing biostatistical procedures such as Kaplan-Meier and logistic regression; and the characterization of performance measures within a complex political, economic and social environment. ULAM provides a means for producing quantitative information to support the selection of a liver allocation policy.