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Clinical decision modeling system.

Shi H, Lyons-Weiler J - BMC Med Inform Decis Mak (2007)

Bottom Line: 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 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Analysis Core, Genomics and Proteomics Core Laboratories, 3343 Forbes Avenue, Pittsburgh, PA 15260 USA. has9@pitt.edu

ABSTRACT

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.

Conclusion: 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.

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Related in: MedlinePlus

The input parameters and tree search summary of the CDMS for a trial run for breast cancer. (a) Control Panel of the run. (b) Tree searching summary.
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Figure 3: The input parameters and tree search summary of the CDMS for a trial run for breast cancer. (a) Control Panel of the run. (b) Tree searching summary.

Mentions: The input file [see Additional file 2] for Breast Cancer Detection Decision Modeling is in Table 1. The SN and SP estimates were derived from the peer-reviewed literature by JLW. In May 2006, NCBI's Pubmed was searched for abstracts with the keywords "breast cancer" AND "sensitivity" AND "specificity". The first 200 abstracts were read and performance evaluation measures of potentially relevant studies were recorded. Because cost estimates for these newly proposed tests are not yet available, all tests were arbitrarily assigned a hypothetical operational cost of $100. Figure 3(a) shows the parameters that are input using the Control Panel. From the Figure, we see that the prevalence for the breast cancer is 0.0013 (The prevalence estimate was obtained from SEER [10] in May, 2006.). All other input parameters are default values. Figure 3(b) shows the tree search summary report. From the summary, we know that there are 12 clinical options (tests) for this search. The second search was performed using 1,000,000 iterations. During the search, 208,824 tree topologies were found that satisfy the performance constraints; 24,156 tree topologies were found that satisfy the cost constraints; and 3,370 tree topologies were found that satisfy both constraints.


Clinical decision modeling system.

Shi H, Lyons-Weiler J - BMC Med Inform Decis Mak (2007)

The input parameters and tree search summary of the CDMS for a trial run for breast cancer. (a) Control Panel of the run. (b) Tree searching summary.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2131745&req=5

Figure 3: The input parameters and tree search summary of the CDMS for a trial run for breast cancer. (a) Control Panel of the run. (b) Tree searching summary.
Mentions: The input file [see Additional file 2] for Breast Cancer Detection Decision Modeling is in Table 1. The SN and SP estimates were derived from the peer-reviewed literature by JLW. In May 2006, NCBI's Pubmed was searched for abstracts with the keywords "breast cancer" AND "sensitivity" AND "specificity". The first 200 abstracts were read and performance evaluation measures of potentially relevant studies were recorded. Because cost estimates for these newly proposed tests are not yet available, all tests were arbitrarily assigned a hypothetical operational cost of $100. Figure 3(a) shows the parameters that are input using the Control Panel. From the Figure, we see that the prevalence for the breast cancer is 0.0013 (The prevalence estimate was obtained from SEER [10] in May, 2006.). All other input parameters are default values. Figure 3(b) shows the tree search summary report. From the summary, we know that there are 12 clinical options (tests) for this search. The second search was performed using 1,000,000 iterations. During the search, 208,824 tree topologies were found that satisfy the performance constraints; 24,156 tree topologies were found that satisfy the cost constraints; and 3,370 tree topologies were found that satisfy both constraints.

Bottom Line: 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 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Analysis Core, Genomics and Proteomics Core Laboratories, 3343 Forbes Avenue, Pittsburgh, PA 15260 USA. has9@pitt.edu

ABSTRACT

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.

Conclusion: 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.

Show MeSH
Related in: MedlinePlus