Comparative Effectiveness of Biomarkers to Target Cancer Treatment: Modeling Implications for Survival and Costs

Background. Biomarkers used at the time of diagnosis to tailor treatment decisions may diffuse into clinical practice before data become available on whether biomarker testing reduces cancer mortality. In the interim, quantitative estimates of the mortality impact of testing are needed to assess the...

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Bibliographic Details
Published in:Medical decision making 2016-07, Vol.36 (5), p.594-603
Main Authors: Birnbaum, Jeanette K., Ademuyiwa, Foluso O., Carlson, Josh J., Mallinger, Leslie, Mason, Mark W., Etzioni, Ruth
Format: Article
Language:English
Subjects:
Biomarkers - metabolism
Breast Neoplasms - therapy
Decision Support Techniques
Female
Humans
Models, Theoretical
Uncertainty
User-Computer Interface
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Summary:Background. Biomarkers used at the time of diagnosis to tailor treatment decisions may diffuse into clinical practice before data become available on whether biomarker testing reduces cancer mortality. In the interim, quantitative estimates of the mortality impact of testing are needed to assess the value of these diagnostic biomarkers. These estimates are typically generated by customized models that are resource intensive to build and apply. Methods. We developed a user-friendly system of models for Cancer Translation of Comparative Effectiveness Research (CANTRANce) to model the mortality impact of cancer interventions. The Diagnostic Biomarker module of this system projects the mortality impact of testing for a diagnostic biomarker, given data on how testing affects treatment recommendations. Costs and quality-of-life outcomes may also be modeled. We applied the Diagnostic Biomarker module to 2 case studies to demonstrate its capabilities. Results. The user interface (http://www.fhcrc.org/cantrance) allows comparative effectiveness researchers to use the Diagnostic Biomarker module of CANTRANce. Our case studies indicate that the model produces estimates on par with those generated by customized models and is a strong tool for quickly generating novel projections. Limitations. The simple structure that makes CANTRANce user-friendly also constrains the complexity with which cancer progression can be modeled. The quality of the results rests on the quality of the input data, which may pertain to small or dissimilar populations or suffer from informative censoring. Conclusions. The Diagnostic Biomarker module of CANTRANce is a novel public resource that can provide timely insights into the expected mortality impact of testing for diagnostic biomarkers. The model projections should be useful for understanding the long-term potential of emerging diagnostic biomarkers.
ISSN:0272-989X
1552-681X
DOI:10.1177/0272989X15601998