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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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The cost summary of the CDMS for a trial run for breast cancer. (a) Normal scale. (b) Logarithmic scale.
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Figure 5: The cost summary of the CDMS for a trial run for breast cancer. (a) Normal scale. (b) Logarithmic scale.

Mentions: The Cost Summary tab (Figure 5(a)) shows the distribution of the costs of all the trees searched. The x-axis shows all possible cost values and y-axis is number of counts. A vertical line is added in the distribution to indicate the value of the cost constraints. In the breast cancer, it is $200.00. The cost constraint line is used to distinguish the area that satisfies the cost constraints with the area that does not, under the distribution curve. Different colors are used to incorporate performance information into the cost distribution. To the left of the cost constraints line, darker blue means that all trees satisfy the cost constraints. Within the dark blue bars, black represents the proportion of trees that also satisfy the performance constraints. The user can change to logarithmic scales to see these parts more clearly (Figure 5(b)). The user only needs to move mouse above the y-axis and right click. Normal scales and logarithmic scales can be toggled by repeating this operation. To the right of the cost constraint line, light blue represents trees that do not satisfy the cost constraints. Light gray pertains to the proportion of trees that satisfy the performance constraints.


Clinical decision modeling system.

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

The cost summary of the CDMS for a trial run for breast cancer. (a) Normal scale. (b) Logarithmic scale.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: The cost summary of the CDMS for a trial run for breast cancer. (a) Normal scale. (b) Logarithmic scale.
Mentions: The Cost Summary tab (Figure 5(a)) shows the distribution of the costs of all the trees searched. The x-axis shows all possible cost values and y-axis is number of counts. A vertical line is added in the distribution to indicate the value of the cost constraints. In the breast cancer, it is $200.00. The cost constraint line is used to distinguish the area that satisfies the cost constraints with the area that does not, under the distribution curve. Different colors are used to incorporate performance information into the cost distribution. To the left of the cost constraints line, darker blue means that all trees satisfy the cost constraints. Within the dark blue bars, black represents the proportion of trees that also satisfy the performance constraints. The user can change to logarithmic scales to see these parts more clearly (Figure 5(b)). The user only needs to move mouse above the y-axis and right click. Normal scales and logarithmic scales can be toggled by repeating this operation. To the right of the cost constraint line, light blue represents trees that do not satisfy the cost constraints. Light gray pertains to the proportion of trees that satisfy the performance 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