Clinical decision modeling system.
BACKGROUND
Decision analysis techniques can be applied in complex situations involving uncertainty and the consideration of multiple objectives. Classical decision modeling techniques require elicitation of too many parameter estimates and their conditional (joint) probabilities, and have not therefore been applied to the problem of identifying high-performance, cost-effective combinations of clinical options for diagnosis or treatments where many of the objectives are unknown or even unspecified.


METHODS
We designed a Java-based software resource, the Clinical Decision Modeling System (CDMS), to implement Naïve Decision Modeling, and provide a use case based on published performance evaluation measures of various strategies for breast and lung cancer detection. Because cost estimates for many of the newer methods are not yet available, we assume equal cost. Our use case reveals numerous potentially high-performance combinations of clinical options for the detection of breast and lung cancer.


RESULTS
Naïve Decision Modeling is a highly practical applied strategy which guides investigators through the process of establishing evidence-based integrative translational clinical research priorities. CDMS is not designed for clinical decision support. Inputs include performance evaluation measures and costs of various clinical options. The software finds trees with expected emergent performance characteristics and average cost per patient that meet stated filtering criteria. Key to the utility of the software is sophisticated graphical elements, including a tree browser, a receiver-operator characteristic surface plot, and a histogram of expected average cost per patient. The analysis pinpoints the potentially most relevant pairs of clinical options ('critical pairs') for which empirical estimates of conditional dependence may be critical. The assumption of independence can be tested with retrospective studies prior to the initiation of clinical trials designed to estimate clinical impact. High-performance combinations of clinical options may exist for breast and lung cancer detection.


CONCLUSIONS
The software could be found useful in simplifying the objective-driven planning of complex integrative clinical studies without requiring a multi-attribute utility function, and it could lead to efficient integrative translational clinical study designs that move beyond simple pair wise competitive studies. Collaborators, who traditionally might compete to prioritize their own individual clinical options, can use the software as a common framework and guide to work together to produce increased understanding on the benefits of using alternative clinical combinations to affect strategic and cost-effective clinical workflows.
Shi
Haiwen
H
Bioinformatics Analysis Core, Genomics and Proteomics Core Laboratories, 3343 Forbes Avenue, Pittsburgh, PA 15260 USA. has9@pitt.edu
Lyons-Weiler
James
J
D001943:Breast Neoplasms / Q000175:diagnosis*; D003299:Cooperative Behavior; D003362:Cost-Benefit Analysis; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D003663:Decision Trees; D019317:Evidence-Based Medicine; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis*; D015397:Program Evaluation; D011381:Programming Languages; D012985:Software Design
Clin Cancer Res. 2005 Sep 15;11(18):6528-35
16166429
Breast. 2002 Dec;11(6):478-83
14965713
Clin Cancer Res. 2005 Feb 1;11(3):1219-25
15709192
Cancer. 2000 Dec 1;89(11 Suppl):2453-6
11147625
BMC Cancer. 2005;5:83
16029516
J Womens Health (Larchmt). 2005 Jan-Feb;14(1):87-95
15692283
AMIA Annu Symp Proc. 2006;:724-8
17238436
Eur J Nucl Med. 2001 Jan;28(1):56-63
11202453
Radiographics. 2001 May-Jun;21(3):641-53; discussion 653-5
11353112
Lung Cancer. 2004 May;44(2):193-8
15084384
Rev Mal Respir. 2003 Nov;20(5 Pt 1):742-52
14631254
Health Technol Assess. 2004 Sep;8(36):iii-iv, ix-xi, 1-158
15361314
Int J Cancer. 2006 Jul 15;119(2):359-64
16477639
Methods Inf Med. 2004;43(3):239-46
15227553
MedGenMed. 2000 Jan 13;2(1):E6
11104452
Clin Breast Cancer. 2003 Aug;4(3):203-9
14499014
Value Health. 2007 May-Jun;10(3):195-203
17532812
Breast Cancer Res Treat. 2006 May;97(2):179-89
16491309
Z Med Phys. 2005;15(4):265-72
16422355
J Natl Cancer Inst. 2001 Sep 19;93(18):1385-91
11562389
J Cell Physiol. 2005 Apr;203(1):1-5
15389637
Ai Zheng. 2002 Nov;21(11):1251-3
12526228
Cancer J. 2002 Jul-Aug;8(4):303-10
12184408
Pharmacoeconomics. 2006;24(11):1043-53
17067190
Radiology. 2002 Dec;225(3):823-8
12461267